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BUGSSGF 2016 Papers - Index
448 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "Don't You SAS Me!": Fostering SAS Adoption in an Excel-based Organization | Andrew W. Clapson | In any organization where people work with data, it is extremely unlikely that there will be only one way of doing things. Things like divisional silos and differences in education, training and subject matter often result in a diversity of tools and levels of expertise. One situation that frequently arises is that of 'code vs. click'; let's call it the difference between code-based, 'power user' data tools and simpler, purely graphic point-and-click tools such as Microsoft EXCEL. Even though the work itself may be quite similar, diffe rences in analysis tools often mean differences in vocabulary and experience, and it can be difficult to convert users of one tool over to another. This discussion will highlight the potential challenges of SAS adoption in an EXCEL-based workplace and propose strategies to gain new SAS advocates in your organization |
excel, sas programmer | source | 2 |
| 2 | 3N Validation to Validate PROC COMPARE Output | Amarnath Vijayarangan | In the clinical research world, data accuracy plays a significant role in delivering quality results. Various validation methods are available to confirm data accuracy. Of these, double programming is the most highly recommended and commonly used method to demonstrate a perfect match between production and validation output. PROC COMPARE is one of the SAS procedures used to compare two data sets and confirm the accuracy. In the current practice, whenever a program rerun happens, the programmer must manually review the output file from the PROC COMPARE to ensure an exact match. This is tedious, time-consuming, and error prone because there are more output files to be reviewed and manual intervention is required. The proposed approach programmatically validates the output of PROC COMPARE in all the programs and generates an HTML output file with a Pass/Fail status flag for each output file. The status is flagged as Pass whenever the output file meets the following 3N criteria: 1 |
proc compare, proc step, data cleaning, clinical trial, ods html | source presentation |
7 |
| 3 | A Descriptive Analysis of Reported Health Issues in Rural Jamaica | Verlin Joseph | Objective: There are currently thousands of Jamaican citizens that lack access to basic healthcare. In order to improve the healthcare system I will collect and analyze data from two clinics in remote locations of the island. This report will analyze data collected from Clarendon parish, Jamaica. Methodology: In order to create a descriptive analysis I will use SAS studio 9.4. A few of the procedures I will use include: PROC IMPORT, PROC MEANS, PROC FREQ, and PROC GCHART. Results: After conducting the aforementioned procedures, I will be able to produce a descriptive analysis of the health issues plaguing the island |
proc step, proc freq, proc gchart, proc import, dataset, proc means | source presentation |
5 |
| 4 | A Guide to Section 508 Compliance Using SAS 9.4 Output Delivery System (ODS) | Glen R. Walker | Disability rights groups are using the court system to exact change. As a result, the enforcement of Section 508 and similar laws around the world has become a priority. When you generate SAS output, you need to protect your organization from litigation. This paper describes concrete steps tha t help you use SAS 9.4 Output Delivery System (ODS) to create SAS output that complies with accessibility standards. It also provides recommendations and code samples that are aligned with the accessibility standards defined by Section 508 and the Web Content Accessibility Guidelines (WCAG 2.0) |
proc step, output delivery system, proc freq, bar chart, dataset, proc gplot, scatter plot, sample | source | 40 |
| 5 | A Macro That Can Fix Data Length Inconsistency and Detect Data Type Inconsistency | Ting Sa | Common tasks that we need to perform are merging or appending SAS data sets. During this process, we sometimes get error or warning messages saying that the same fields in different SAS data sets have different lengths or different types. If the problems involve a lot of fields and data sets, we need to spend a lot of time to identify those fields and write extra SAS codes to solve the issues. However, if you use the macro in this paper, it can help you identify the fields that have inconsistent data type or length issues. It also solves the length issues automatically by finding the maximum field length among the current data sets and assigning that length to the field. An html report is generated after running the macro that includes the information about which fields' lengths have been changed and which fields have inconsistent data type issues | create table, group by, proc step, %if-%then-%else | source | 7 |
| 6 | A Multistage Modeling Strategy for Hierarchical Demand Forecasting | Pu Wang | Although rapid development of information technologies in the past decade has provided forecasters with both huge amounts of data and massive computing capabilities, these advancements do not necessarily translate into better forecasts. Different industries and products have unique demand patterns. There is not yet a one-size-fits-all forecasting model or technique. A good forecasting model must be tailored to the data in order to capture the salient features and satisfy the business needs. This paper presents a multistage modeling strategy for demand forecasting. The proposed strategy, which has no restrictions on the forecasting techniques or models, provides a general framework for building a forecasting system in multiple stages | time series analysis, seasonality, feature extraction, price elasticity, stage 2, stage 1, correlation, regression analysis | source | 11 |
| 7 | A Practical Introduction to SAS Data Integration Studio | Erik S. Larsen, Frank A. Ferriola |
A useful and often overlooked tool which SAS released with its Business Intelligence suite to aid in ETL is the Data Integration Studio (or DI Studio) product. DI Studio provides end users with the ability to extract, transform, join and load data from various DBMSs, data marts and other data stores using a graphical interface and without needing to code different credentials for each schema. It will allow for seamless promotion of code to a production system without the need to alter the code. This tool is quite useful for deploying and being able to schedule jobs using the schedule man ager in SAS Management Console as all code created by DI Studio is optimized. While this tool allows users to create code from scratch, one of its most useful tasks is that it can take legacy SAS code and with minimal alterations, can have its data associations created and have all of the properties of a job coded from scratch | sas data integration studio, sas base, dataset, sas administrator, sas enterprise guide | source | 16 |
| 8 | A Prompted Application to Easily Create Forest Plots and Inner Margin Tables | Ted Durie | Two of the powerful features of ODS Graphics is the ability to create Forest Plots and add Inner-Margin tables to graphics output. The drawback, however, is the syntax required from PROC TEMPLATE is complex and tedious. A prompted application or even a parameterized stored process that connects PROC TEMPLATE code to a point and click application would make the task of generating the fore-mentioned output much easier |
proc step, proc freq, sas enterprise guide, forest plot, proc template, macro variable, sas management console, macro facility | source zip |
21 |
| 9 | A Property and Casualty Insurance Predictive Modeling Process in SAS | Mei Najim | Predictive analytics is an area that has been developing rapidly in property & casualty insurance companies over the past two decades. Although the statistical foundations of predictive analytics have large overlaps, the business objectives, data availability, and regulations are different across property & casualty insurance, life insurance, banking, pharma ceutical, and genetics industries, etc. A property & casualty insurance predictive modeling process with large data sets will be introduced including data acquisition, data preparation, variable creation, variable selection, model building (a.k.a.: model fitting), model validation, and model testing. Variable selection and model validation stages will be introduced in more detail. Some successful models in the insurance companies will be introduced. Base SAS , SAS Enterprise Guide , and SAS Enterprise Miner are presented as the main tools for this process | data mining, data cleaning, feature selection, response variable, proc hpsplit, casualty insurance, regression analysis, logistic regression, model validation, neural network, sas enterprise guide, sas base, univariate analysis, significance level, logistic distribution, insurer, model testing, predictive analytics, stepwise selection, validation set, proc genmod, data transformation, incurred loss, insurance, model training, feature engineering, correlation | source | 14 |
| 10 | A Pseudo-Interactive Approach to Teaching SAS Programming | Hunter Glanz | With the advent of the exciting new hybrid field of Data Science, programming and data management skills are in greater demand than ever and have never been easier to attain. Online resources like codecademy and w3schools offer a host of tutorials and assistance to those looking to develop their programming abilities and knowledge. Though their content is limited to languages and tools suited mostly for web developers, the value and quality of these sites are undeniable. To this end, similar tutorials for other free-to-use software applications are springing up. The interactivity of these tutorials elevates them above most, if not all, other out- of-classroom learning tools. The process of learning programming or a new language can be quite disjointed when trying to pair a textbook or similar walk-through material with matching coding tasks and problems | data science, data governance | source zip |
6 |
| 11 | A Report Tracking Framework - Using SAS to Report Status of Scheduled Jobs ePoster 11540 | Jia Heng | Presentation | source | 4 | |
| 12 | A Ringside Seat: The ODS Excel Destination versus the ODS ExcelXP Tagset | Chevell T. Parker | The new and highly anticipated SAS Output Delivery System (ODS) destination for Microsoft EXCEL is finally here! Available as a production feature in the third maintenance release of SAS 9.4 (TS1M3), this new destination generates native EXCEL (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who uses the |
excel, format statement, proc step, output delivery system, ods excel, macro variable, proc import, datetime20., options statement, proc sgplot | source | 32 |
| 13 | A SAS Macro for Generating a Ternary Graph in a Ceramics Industry Process | Igor Ferreira do Nascimento, Zacarias Linhares, Roberto Arruda Lima Soares |
This work presents a macro for generating a ternary graph that can be used to solve a problem in the ceramics industry. The ceramics industry uses mixtures of clay types to generate a product with good properties that can be assessed according to breakdown pressure after incineration, the porosity, and water absorption. Beyond these properties, the industry is concerned with managing geological reserves of each type of clay. Thus, it is important to seek alternative compositions that present properties similar to the default mixture. This can be done by analyzing the s urface response of these properties according to the clay composition, which is easily done in a ternary graph. SAS documentation does not describe how to adjust an analysis grid in nonrectangular forms on graphs. A macro Triaxial, however, can generate ternary graphical analysis by creating a special grid, using an annotated database, and making linear transformations of the original data | proc step, response variable | source | 9 |
| 14 | A SAS Macro for Generating Random Numbers of Skew-Normal and Skew-T Distributions | Alan Ricardo da Silva, Paulo Henrique Dourado da Silva |
This paper aims to show a SAS macro for generating random numbers of skew normal and skew t distributions as well as the quantiles of these dis tributions. The results are similar to those genera ted by 'sn' package of R software | student t distribution, normal distribution | source zip presentation |
19 |
| 15 | A SAS Macro for Geographically Weighted Negative Binomial Regression | Alan Ricardo da Silva, Thais Rodrigues |
Geographically Weighted Negative Binomial Regression (GWNBR) was developed by Silva and Rodrigues (2014) and it is a generalization of Geographically Weighted Poisson Regression (GWPR) proposed by Nakaya et al. (2005) and of Poisson and negative bi nomial regressions. This paper aims to show a SAS macro to estimate GWNBR model encoded in SAS/IML and using PROC GMAP to draw the maps |
regression analysis, poisson distribution, binomial distribution, negative binomial regression, estimate, conditional do loop, macro loop, likelihood function, negative binomial distribution, response variable, %if-%then-%else, macro definition | source zip |
20 |
| 16 | A SAS Program to Analyze High Dimensional Sparse Counts Data Using Lumping and Splitting Approach | Xiaoli Lu | Presentation | source | 4 | |
| 17 | A Sea of Data--Gaining Competitive Advantage by Creating a Portal of Seafood Insights in SAS Visual Analytics Designer and SAS Information Delivery Portal | Kia Uuskartano, Tor Erik Somby |
This paper presents how Norway, the world's second-largest seafood-exporting country, shares valuable seafood insight using the SAS Visual Analytics Designer. Three complementary data source s: trade statistics, consumption panel data, and consumer survey data, are used to strengthen the knowledge and understanding about the important markets for seafood, which is a potential competitive advantage for the Norwegian seafood industry. The need for information varies across users and as the amount of data available is growing, the challenge is to make the information available for everyone, everywhere, at any time. Some users are interested in only the latest trade developments, while others worki ng with product innovation are in need of deeper consumer insights. Some have quite advanced analytical skills, while others do not | excel, data wrangling | source | 8 |
| 18 | A Second Look at the ODS Destination for PowerPoint | Tim Hunter | This paper demonstrates how to use the ODS destination for PowerPoint to create attractive presentations from your SAS output. Packed with examples, this paper gives you a behind-the-scenes tour of how ODS creates Microsoft PowerPoint presentations. You get an in-depth look at how to customize the ODS PowerPoint style templates that control the appearance of your presenta tion. With this information you can quickly turn your SAS output into an engaging and informative presentation. This paper is a follow-on to the SAS Global Forum 2013 paper "A First Look at the ODS Destination for PowerPoint." |
sas output, proc step, proc template, output delivery system, footnote statement, options statement | source zip |
28 |
| 19 | A Short Introduction to Longitudinal and Repeated Measures Data Analyses | Leanne Goldstein | Longitudinal and repeated Measures data are seen in nearly all fields of analysis. Examples of this data include weekly lab test results of patients or test scores by children from the same class. Statistics students and analysts alike may be overwhelmed when it comes to repeated measures or longitudinal data analyses. They may try to educate themselves by diving into text books or taking semester long or intensive weekend courses resulting in even more confusion. Some may try to ignore the repeated nature of data and take short cuts such as analyzing all data as independent observations or analyzing summary statistics such as averages or changes from first to last points and ignoring all the data in-between. This Hands-On presentation will introduce longitudinal and repeated measures analyses without heavy emphasis on theory. Students in the workshop will have the opportunity to get hands-on experience graphing longitudinal and repeated measures data | proc step, proc mixed, repeated measures, fixed effects, covariance, proc reg, estimate, repeated measures anova, regression analysis, correlation, likelihood function, longitudinal data, anova, maximum likelihood estimation, data science, proc genmod, proc sgplot, nominal variable, random intercept, proc glimmix, proc sort, covariance structure, simple linear regression, restricted maximum likelihood, main effect, spaghetti plot | source | 19 |
| 20 | A Silver Lining in the Cloud: Deployment of SAS Visual Analytics 7.2 on AWS | Vimal Raj, Rajesh Inbasekaran |
Amazon Web Services (AWS) as a platform for analytics and data warehousing has gained significant adoption over the years. With SAS Visual Analytics being one of the preferred tools for data visualization and analytics, it is imperative to be able to deploy SAS Visual Analytics on AWS. This ensures swift analysis and reporting on large amounts of data with SAS Visual Analytics by minimizing the movement of data across environments. This paper focuses on installing SAS Visual Analytics 7.2 in an Amazon Web Services environment, migration of metadata objects and content from previous versions to the deployment on the cloud, and ensuring data security | spk file, sas visual analytics, data cleaning | source | 19 |
| 21 | A Survey of Some of the Most Useful SAS Functions | Ronald P. Cody | SAS Functions provide amazing power to your DATA STEP programming. Some of these functions are essential- some of them save you writing volumes of unnecessary code. This paper covers some of the most useful SAS functions. Some of these functions may be new to you and they will change the way you program and approach common programming tasks |
character variable, missing value, length statement, substr function, input statement, lag function, compress function, sas function, put statement, sysdate9, yrdif, character functions, numeric functions, call missing, call sortn, missing function, if-then-else | source | 16 |
| 22 | A Text Analytics Approach to Unraveling Trends in | Sairam Tanguturi, Sagar Rudrake, Nithish Yeduguri |
Presentation | source | 4 | |
| 23 | A Waze App for Base SAS: Automatically Routing Around Locked Data Sets, Bottleneck Processes, and Other Traffic Congestion on the Data Superhighway | Troy Martin Hughes | The Waze application, purchased by Google in 2013, alerts millions of users about traffic congestion, collisions, construction, and other complexities of the road that can stymie motorists' attempts to get from A to B. From jackknifed rigs to jackalope carcasses, roads can be gnarled by gridlock or littered with obstacles that impede traffic flow and efficiency. Waze algorithms automatically reroute users to more efficient routes based on user-reported events as well as historical norms that demonstrate typical road conditions. Extract, transform, load (ETL) infrastructures often represent serialized process flows that can mimic highways, and which can become similarly snarled by locked data sets, slow processes, and other factors that introduce inefficiency. The LOCKITDOWN SAS macro, introduced at WUSS in 2014, detects and prevents data access collisions that occur when two or more SAS processes or users simultaneously attempt to access the same SAS data set | sas base, macro call, sysrc, macro definition | source | 5 |
| 24 | Accelerate the Time to Value by Implementing a Semantic Layer Using SAS Visual Analytics | Arun Sugumar, Vignesh Balasubramanian, Harsh Sharma |
Although business intelligence experts agree that empowering businesses through a well-constructed semantic layer have undisputed benefits, a successful implementation has always been a formidable challenge. This presentation highlights the best practices to follow and mistakes to avoid, leading to a successful semantic layer implementation by using SAS Visual Analytics. A correctly implemented semantic layer provides business users with quick and easy access to information for analytical and fact - based decision-making. Today, everyone talks about how the modern data platform enables bu sinesses to store and analyze big data, but we still see most businesses trying to generate value from the data that they already store. From self-service to data visualization, business intelligence and descriptive analytics are still the key requirements for any business, and we discuss how to use SAS Visual Analytics to address them all | business intelligence, data governance, star schema, fact table, dimension table, data cleaning, master data management | source | 12 |
| 25 | Accessing PubMed Data using SAS and the Entrez Programming Utilities | Craig Hansen | Presentation | source | 6 | |
| 26 | Advanced ETL Scheduling Techniques with SAS Data Integration Studio and IBM Platform LSF | Angus Looney | Presentation | source | 6 | |
| 27 | Advanced Topics in SAS Environment Manager | Zhiyong Li, Gilles Chrzaszcz |
Since it was first released three years ago, SAS Environment Manager has been used widely in many customers' production environment. Customers are now familiar with the basic functions of the product. They are asking for more information about advanced topics such as how to manage users, roles, and permissions; how to secure the product; and how to interact with their existing system management tools in their enterprise environment. This paper addresses those advanced but practical issues from a real customer's perspective. The paper first briefly lists what's new in the most current release of SAS Environment Manager. It then discusses the new user management design introduced in the third maintenance release of SAS 9.4. We explain how that design addresses customers' concerns and the best practices for using that design and for setting up user roles and permissions. We also discuss the new process and best practices for configuring SSL for SAS Environment Manager | data mart, sas management console | source | 20 |
| 28 | Agile BI: How Eandis is using SAS Visual Analytics for Energy Grid Management | Olivier Goethals | Eandis, a Belgian energy distribution grid operator, is using Visual Analytics to explore data from various sources, including SAP Business Warehouse, to better plan maintenance and investments in the grid. To achieve this, a new agile way of thinking about Business Intelligence was needed | sas enterprise guide, raw data, business intelligence, data warehouse, excel | source | 8 |
| 29 | Alerts Don't Launder Money (and Finance Terrorism)--People Do! | Kathy Hart | For far too long, anti-money laundering and terrorist financing solutions have forced analysts to wade through oceans of transactions and alerted work items (alerts). Alert-centered analysis is both ineffective and costly. The goal of an anti-money laundering program is to reduce the risk for your financial institution, and to do this most effectively, you must start with analysis at the customer level, rather than simply troll through volumes of alerts and transactions. This paper discusses how a customer-centric approach leads to increased analyst efficiency and streamlined investigations. Rather than starting with alerts and transactions, starting with a customer-centric view allows your analysts to rapidly triage suspicious activities, prioritize work, and quickly move to investigating the highest risk customer activities | machine learning, aml, suspicious activity, type i error | source | 7 |
| 30 | All Aboard! Next Stop is the Destination Excel | William E. Benjamin Jr. | Over the last few years both Microsoft EXCEL file formats and the SAS interfaces to those EXCEL formats have changed. SAS has worked hard to make the interface between the two systems easier to use. Starting with "Comma Separated Variable" files and moving to PROC IMPORT and PROC EXPORT, LIBNAME processing, SQL processing, SAS Enterprise Guide, JMP, and then on to the HTML and XML TAGSETS like MSOFFICE2K, and EXCELXP. Well, there is now a new entry into the processes available for SAS users to send data directly to EXCEL. This new entry into the ODS arena of data transfer to EXCEL is the ODS destination called EXCEL. This process is included within SAS ODS and produces native format EXCEL files for version 2007 of EXCEL and later. It was first shipped as an experimental version with the first maintenance release of SAS 9.4. This ODS destination has many features similar to the EXCELXP TAGSETS |
excel, proc step, xls, output delivery system, by group processing, ods excel | source zip |
15 |
| 31 | An Analysis of Medicare Provider Utilization and Payment Data: A Focus on the Top 5 DRGs and Mental Health Care | Joo Ann Lee, Micheal Friendly, Cathy Labrish |
bubble plot, group by, cluster analysis, outlier | source | 28 | |
| 32 | An Application of the DEA Optimization Methodology to Optimize the Collection Direction Capacity of a Bank | Miguel Díaz, Oscar Javier Cortés Arrigui |
In the Collection Direction of a we ll-recognized Colombian financial institution, there was no methodology that provided an optimal number of collection agents, to improve the collection task and make possible that more customers be compromised with their minimum monthly payment of their de bt. The objective of this paper is to apply the Data Envelopment Analysis (DEA) Optimization Methodology, to determine the optimal number of agents to maximize the monthly collection in the bank. We show that the results can have a positive impact in the c redit portfolio behavior and reduce the collection management cost . DEA optimization methodology has been successfully used in various fields to solve multi -criteria optimization problems, but it is not commonly used in the financial sector mostly because this methodology requires specialized software, such as SAS Enterprise Guide | debt collection, odds ratio, sas enterprise guide | source zip |
12 |
| 33 | An Application of the PRINQUAL Procedure to Develop a Synthetic Index of Customer Value for a Colombian Financial Institution | Ivan Atehortua, Diana Flórez |
Colpatria, as a part of Scotiabank in Colombia, has several methodologies that enable us to have a vision of the customer from a risk perspective. However, the current trend in the financial sector is to have a global vision that involves aspects of risk as well as of profitability and customer loyalty. As a part of the business strategies to develop cross -sell and to loyal customer, our bank is currently focused exclusive in a Risk vision. With this paper, we propose a new methodology which allows to rank our customers from goods to bad customers with a more complete vision. In this paper, we estimate a synthetic index to know the real value of the client and it includes not only risk as profitability, loyalty and customer income. In order to generate the Index of Customer Value, we construct a synthetic index using principal component analysis. As result, we obtained a unique measure of value | proc princomp, correlation, covariance, ordinal variable | source | 7 |
| 34 | An Efficient Algorithm in SAS to Reconcile Two Different Serious Adverse Event (SAE) Data Sources | Zhou Hui | IN THE REGULATORY WORLD OF PATIENT SAFETY AND PHARMACOVIGILANCE, WHETHER IT'S DURING CLINICAL TRIALS OR POST-MARKET SURVEILLANCE, SAES THAT AFFECT PARTICIPANTS MUST BE COLLECTED, AND IF CERTAIN CRITERIA ARE MET, REPORTED TO THE FDA AND OTHER REGULATORY AUTHORITIES. SAES ARE OFTEN ENTERED INTO MULTIPLE DATABASES BY VARIOUS USERS, RESULTING IN POSSIBLE DATA DISCREPANCIES AND QUALITY LOSS. EFFORTS HAVE BEEN MADE TO RECONCILE THE SAE DATA BETWEEN DATABASES, BUT THERE IS NO INDUSTRIAL STANDARD REGARDING THE METHODOLOGY OR TOOL EMPLOYED FOR THIS TASK. SOME ORGANIZATIONS STILL RECONCILE THE DATA MANUALLY, WITH VISUAL INSPECTIONS AND VOCAL VERIFICATION. NOT ONLY IS THIS LABORIOUS AND ERROR-PRONE, IT BECOMES PROHIBITIVE WHEN THE DATA REACH HUNDREDS OF RECORDS. WE DEVISED AN EFFICIENT ALGORITHM USING SAS TO COMPARE TWO DATA SOURCES AUTOMATICALLY. OUR ALGORITHM IDENTIFIES MATCHED, DISCREPANT, AND UNPAIRED SAE RECORDS | clinical database, clinical trial, stored procedure, sample, adverse events | source | 7 |
| 35 | An Efficient Pattern Recognition Approach with Applications | Patrick Hall, Jorge Silva, Ilknur Kaynar Kabul, Keith R. Holdaway, Alex Chien |
This paper presents supervised and unsupervised pattern recognition techniques that use Base SAS and SAS Enterprise Miner software. A simple preprocessing technique creates many small image patches from larger images. These patches encourage the learned patterns to have local scale, which follows well-known statistical properties of natural images. In addition, these patches reduce the number of features that are required to represent an image and can decrease the training time that algorithms need in order to learn from the images. If a training label is available, a classifier is trained to identify patches of interest. In the unsupervised case, a stacked autoencoder network is used to generate a dictionary of representative patches, which can be used to locate areas of interest in new images. This technique can be applied to pattern recognition problems in general, and this paper presents examples from the oil and gas industry and from a solar power forecasting application | data mining, sas base | source | 9 |
| 36 | An End-to-End, Automation Framework for Implementing Identity-Driven Row-Level Security Using SAS Visual Analytics | Paul W. Johnson, Ekaitz Goienola, Dileep Pournami |
At the Royal Bank of Scotland (RBS), business intelligence users require sophisticated security permissions both at object level and data (row) level in order to comply with data security, audit, and regulatory requirements. When we rolled out SAS Visual Analytics to our main stakeholder groups, this was identified as a key requirement as data is no longer restricted to the desktop but is increasingly available on mobile devices such as tablets and smart phones. Implementing row -level security (RLS) controls, in addition to standard security measures such as authentication, is a most effective final layer in your data authorization process. RLS procedures in leading relational database management systems (RDBMSs) and business intelligence (BI) software are fairly common place, but with the emergence of big data and in-memory visualization tools such as SAS Visual Analytics, those RLS procedures now need to be extended to the memory interface | raw data, sample | source | 12 |
| 37 | An Insider's Guide to SAS/ACCESS Interface to Impala | Jeffrey Bailey | Impala is an open-source SQL engine designed to bring real-time, concurrent, ad hoc query capability to Hadoop. SAS/ACCESS Interface to Impala allows SAS to take advantage of this exciting technology. This presentation uses examples to show you how to increase your program's perfor mance and troubleshoot problems. We will discuss how Impala fits into the Hadoop ecosystem and how it differs from Hive. Learn the differences between the Hadoop and Impala SAS/ACCESS engines |
libname statement, proc sql, create table, proc step, cpu time, sas log, sql pass-through, sas library, set statement, sas base, select, batch processing, filename statement, proc rank, label statement, order by, variable | source | 19 |
| 38 | An Introduction to SAS Arrays | Andrew T. Kuligowski, Lisa A. Mendez |
So, you've heard about SAS arrays, but are not sure when - or why - you would use them. This presentation will provide the attendee / reader with a background in SAS arrays, from an explanation as to what occurs during compile time through to their programmatic use, and will include a discussion regarding how DO-loops and macro variables can enhance array usability. Specific examples, including Fahrenheit to Celsius temperature conversion, salary adjustments, and data transposition / counting will assist the user with effective use of SAS arrays in their own work, and provide a few caveats as to their usage, as well | do loop, array, sample, proc step | source | 11 |
| 39 | Analysis of Grades for University Students Using Administrative Data and the IRT Procedure | Sara Armandi | This study is the first to implement item response theory on an entire educational program rather than on a single test. I use the new IRT procedure, introduced in SAS/STAT 13.1, to compare the difficulty and use of grading scale across compulsory courses at the bachelor's program in economics at the University of Copenhagen. Further, the latent ability traits estimated for all students in the sample by PROC IRT are used as predictors in a logistic regression model. The hypothesis is that students who have a lower ability trait will have a greater probability of dropping out than students with a higher ability. In the paper, three unidimensional item response models, two dichotomous and one polytomous, are applied on data from one cohort of students (n=236). The results suggest that in order to receive the highest possible grade, the highest level of student ability is needed for the course exam in the first-year course Descriptive Economics A |
logistic regression, regression analysis, logistic distribution, proc step, confidence interval, estimate, proc reg, maximum likelihood estimation, likelihood function, sample, response variable | source | 13 |
| 40 | Analysis of IMDb Reviews for Movies and Television Series Using SAS Enterprise Miner and SAS Sentiment Analysis Studio | Ameya Jadhavar, Goutam Chakraborty, Prithvi Raj Sirolikar |
Movie reviews provide crucial information and act as an imp ortant factor when deciding whether or not to spend money on seeing a film i n the theater. Each review reflects an individual's take on the movie and there are often contrasting reviews for the same movie. G oing through each review may create confusion in the mind of a reader. Analyzing all the movie reviews and generating a quick summary that describes the perfor mance, direction, screenplay among other aspects will be helpful to the readers in understanding the sentiments of movie reviewers and to decide if they would like to watch the movie in the theatres. In this paper, we demonstrate how the SAS Enterprise Miner nodes enable us to generate a quick summary of the terms and their relationship with each other which describe the various aspects of a movie. The Text Cluster and Text Topic nodes are used to generate groups with similar subjects such as genres of movies, acting and music | parsing, validation set, proc freq, data cleaning, data mining, response variable, relative frequency, training set | source | 17 |
| 41 | Analytics and Data Management in a Box: Dramatically Increase Performance | Greg Otto, Tho H. Nguyen, Paul Segal |
Organizations are collecting more structured and unstructured data than ever before, and it is presenting great opportunities and challenges to analyze all of that complex data. In this volatile and competitive economy, there has never been a bigger need for proactive and agile strategies to overcome these challenges by applying the analytics directly to the data rather than shuffling data around. Teradata and SAS have joined forces to revolutionize your business by providing enterprise analytics in a harmonious data management platform to deliver critical strategic insights by applying advanced an alytics "inside" the database or data warehouse where the vast volume of data is fed and resides | proc step, proc sql, in-database processing, teradata, data governance, sas model manager, data integration, sas base, model deployment, parallel processing, in-memory analytics, data mining | source | 14 |
| 42 | Analytics of Things: Golf a Good Walk Spoiled? | David Shannon | This paper demonstrates techniques using SAS software to combine data from devices and sensors for analytics. Base SAS, Data Integration Studio and Visual Analytics are used to query external services, import, fuzzy match, analyze and visualize data. Finally, the benefits of SAS Event Stream Processing models and alerts will be discussed. To bring the analytics of things to life the following data are collected: GPS data from countryside walks, GPS and score card data from smart phones whilst playing golf and meteorological data feeds. These data are combined to test the old adage that golf is a good walk spoiled. Further, the use of alerts and potential for predictive analytics is discussed | web service, proc step, iot, proc http, stream processing, real-time processing, machine learning, vital signs, proc sql, character functions | source | 15 |
| 43 | Analyzing Non-Normal Binomial and Categorical Response Variables under Varying Data Conditions | Niloofar Ramezani | When dealing with non-normal categorical response variables, logistic regression is the robust method to use for modeling the relationship between categorical outcomes and different predictors without assuming a linear relationship between them. Within such models, the categorical outcome may be binary, multinomial, or ordinal and predictors may be continuous or categorical. Another complexity that might be added to such studies is when data are longitudinal, such as when outcomes are collected at multiple follow-up times. Learning about modeling such data within any statistical method is beneficial because it allows researchers to look at changes over time. This study looks at several methods of modeling binary and categorical response variables within regression models using real-world data. Starting with the simplest case of binary outcomes, through ordinal outcomes, this study looks at different modeling options within SAS and includes longitudinal cases for each model | logistic distribution, logistic regression, proc step, regression analysis, likelihood function, correlation, response variable, multinomial logistic regression, proc genmod, proc logistic, proc mixed, ordinal logistic regression, missing value, proc glimmix, longitudinal data, maximum likelihood estimation, proc calis, conditional logistic regression, odds ratio, covariance, sample, predictor variable, repeated measures, fixed effects, significance level, imputation, covariance structure, data science, estimate, binomial distribution, proc phreg | source | 16 |
| 44 | Analyzing the Hospital Episode Statistics Data Set: How Much Does SAS Help? | Violeta Balinskaite | Hospital Episode Statistics (HES) is a data set containing records of all admissions, outpatient appointments and accident and emergency (A&E) attendances at National Health Service (NHS) hospitals in England. Each year over 125 million admitted patient, outpatient and A&E records are processed. Such a large data set enables rich research opportunities for researchers and health care professionals. However, patient care data is complex and can be difficult to manage. This paper demonstrates the flexibility and power of SAS programming tools such as DATA STEP, PROC SQL and Macros to help to analyze HES |
regression analysis, logistic regression, binomial distribution, proc step, logistic distribution, proc sql, bootstrap, sample, poisson regression, proc sort, poisson distribution | source | 9 |
| 45 | Annotating the SAS ODS Graphics Way! | Dan Heath | For some users, having an annotation facility is an integral part of creating polished graphics for their work. To meet that need, we created a new annotation facility for the SAS ODS Graphics (SG) procedures in SAS 9.3. Now, with SAS 9.4, the Graph Template Language (GTL) supports annotation as well! In fact, the GTL annotation facility has some unique features not available in the SG procedures, such as using multiple sets of annotation in the same graph and the ability to bind annotation to a particular cell in the graph. This presentation will cover some basic concepts of annotating that are common to both GTL and the SG procedures. Then, I will apply those concepts to demonstrate some unique abilities of GTL annotation |
proc step, proc sgplot, bubble plot, proc template | source | 15 |
| 46 | Application of Data Mining Techniques in Improving Breast Cancer Diagnosis | Josephine Akosa | Breast cancer is the second leading cause of cancer deaths among women in the United States. Although mortality rates have been decreasing over the past decade, it is important to continue to make advances in diagnostic procedures as early detection vastly improves chances for survival. Our goal for this study is to identify a data mining model that accurately predicts the presence of a malignant tumor using data from fine needle aspiration (FNA) with visual interpretation. Compared with other methods of d iagnosis, FNA displays the highest likelihood for improvement i n sensitivity. Furthermore, we aim to identify the variables most closely associated with accurat e outcome prediction. We utilize the Wisconsin Breast Cancer dataset which contains 699 clinical case samples (65.52% benign and 34.48% malignant) assessing the nuclear features of the FNA | feature selection, proc princomp, data mining, proc hpsplit, support vector machine, proc hpforest, neural network, boosting, regression analysis, gradient boosting, logistic regression, data cleaning, correlation, response variable, sensitivity analysis, logistic distribution, ks statistic, validation set, feature importance, sample | source presentation |
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| 47 | Application of Gradient Boosting through SAS Enterprise Miner to Classify Human Activities | Minh Pham, Mary Ruppert-Stroescu, Mostakim Tanjil |
Using smart clothing with wearable medical sensors integrated to keep track of human health is now attracting many researchers. However, body movement caused by daily human activities inserts artificial noise into physiological data signals, which affects the output of a health monitoring/alert system. To overcome this problem, recognizing human activities, determining relationship between activities and physiological signals, and removing noise from the collected signals are essential steps. This paper focuses on the first step, which is human activity recognition. Our research shows that no other study used SAS for classifying human activities. For this study, two data sets were collected from an open repository. Both data sets have 561 input variables and one nominal target variable with four levels. Principal component analysis along with other variable reduction and selection techniques were applied to reduce dimensionality in the input space | gradient boosting, proc hpsplit, feature selection, data mining, response variable, neural network, data cleaning, proc princomp, scree plot, validation set, proc varreduce | source presentation |
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| 48 | Applying Frequentist and Bayesian Logistic Regression to MOOC data in SAS: a Case Study | Yan Zhang, Ryan Baker, Yoav Bergner |
Massive Open Online Courses (MOOCs) have attracted increasing attention in educational-data-mining research. MOOC platforms provide free high education courses to Internet users worldwide. However, MOOCs have high enrollment but notoriously low completion rates. The goal of this study is to use frequentist and Bayesian logistic regressions to investigate whether and how students' engagement, intentions, education levels, and other demographics are conducive to MOOC course completion. The original data used in this study came from an online 8-week course titled "Big Data in Education" taught within the Coursera platform (MOOC) by Teachers College, Columbia University. The datasets for analysis were created from three different sources - clickstream logs, a voluntary pre-course survey, and homework assignments. The SAS system offers multiple procedures to perform logistic regression, with each procedure having different features and functions | logistic regression, logistic distribution, regression analysis, proc step, bayesian statistics, nominal variable, proc freq, proc surveylogistic, sample, interval variable, maximum likelihood estimation, likelihood function, proc genmod | source presentation |
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| 49 | Architecting Data Management: Seven Principles Using SAS, DataFlux, and SQL | Bob Janka | Seven principles frequently manifest themselves in data management projects. These principles improve two aspects: the development process, which enables the programmer to deliver better code faster with fewer issues; and the actual performance of programs and jobs. Using examples from Base SAS, SAS Macro Language, DataFlux, and SQL, this paper will present seven principles: environments; job control data; test data; improvement; data locality; minimal passes; and indexing. Readers with an intermediate knowledge of SAS DataFlux Management Platform and/or Base SAS and SAS Macro Language as well as SQL will understand these principles and find ways to use them in their data management projects | proc sql, macro variable, proc step, data validation rules, macro put, sas base, sample, macro definition, data governance, raw data, proc print, proc means, cpu time, group by, sas output, order by, sas programmer, macro facility | source | 20 |
| 50 | Architecting Your SAS Grid: Networking for Performance | Tony Brown | With the popularity of network-attached storage for shared file systems, IT shops are increasingly turning to them for SAS Grid implementations. Given the high I/O demand of SAS large block processing, how do you ensure good performance between SAS Grid nodes and network-attached storage? This paper discusses different types of network-attached implementations, what works, and what does not. It provides advice for bandwidth planning, types of network technology to use, and what has been practically successful in the field | sas grid, dataset | source | 11 |
| 51 | Architecture and Deployment of SAS Visual Analytics 7.2 with a Distributed SAS LASR Analytic Server for Showcasing Reports to the Public | Vimal Raj, Ratul Saha |
This paper demonstrates the deployment of SAS Visual Analytics 7.2 with a distributed SAS LASR Analytic Server and an internet-facing web tier. The key factor in this deployment is to establish the secure web server connection using a third-party certificate to perform the client and server authentication. The deployment process involves the following steps: 1) Establish the analytics cluster, which consists of SAS High-Performance Deployment of Hadoop and the deployment of high- performance analytics environment master and data nodes. 2) Get the third-party signed certificate and the key files. 3) Deploy the SAS Visual Analytics server tier and middle tier. 4) Deploy the standalone web tier with HTTP protocol configured using secure sockets. 5) Deploy the SAS Web Infrastructure Platform. 6) Perform post-installation validation and configuration to handle the certificate between the servers | data cleaning, grid computing | source | 9 |
| 52 | Arrays and DO Loops: Applications to Health-Care Diagnosis Fields | Ryan Ferland | Arrays and Do loops have been used for years by SAS programmers who work with diagnosis fields in health-care data, and the opportunity to use these techniques has only grown since the launch of the Affordable Care Act (ACA) in the United States. Users new to SAS or to the health-care field may find an overview of existing (as well as new) applications helpful. Risk adjustment software, including the publicly available Health and Human Services (HHS) risk soft ware that uses SAS and was released as part of the ACA implementation, is one example of code that is significantly improved by the use of arrays. Similar projects might include evaluations of diagnostic persistence, comparisons of diagnostic frequency or intensity between providers, and checks for unusual clusters of diagnosed conditions. This session reviews examples suitable for intermediate SAS users, including the application of two- dimensional arrays to diagnosis fields | do loop, sas programmer | source | 10 |
| 53 | Arrest Prediction and Analysis based on a Data Mining Approach | Karan Rudra, Goutam Chakraborty, Maitreya Kadiyala |
In lieu of past indiscretions by the police departments leading to riots in major U.S cities, it is important to assess factors leading to arrest of a citizen. The police department should understand the arrest situation before they can take any decision and diffuse any possibility of a riot or similar incidents. Many such incidents in the past are a result of police department failing to make right decisions in the case of emergencies. The primary objective of this study is to understand the key factors that impact arrest of a person in New York City and understanding various crimes that have taken place in the region in the last two years. The study explores different regions of New York City where the crimes have taken place and also the timing of incidents leading to them. The study explores different regions of New York City like Manhattan, Brooklyn, Queens, Bronx and Staten Island where the crimes have taken place and also the timing of incidents leading to it | data cleaning, regression analysis, polynomial regression, proc hpsplit, confusion matrix, suspicious activity, data mining | source | 9 |
| 54 | Ask the Experts: Output Delivery System (ODS) Graphics | Warren F. Kuhfeld | Presentation | proc step, sg annotation, call execute | source zip |
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| 55 | Assessing SAS Skill Levels during Interviews | Jenine Milum | This paper provides tools and guidelines to assess SAS skill level during the interview process. Included are interview questions for Base SAS developers, statisticians, and SAS administration candidates. Whether the interviewer has a deep understanding or just some familiarity with these areas, the questions provided will allow discussions to uncover skills and usage experience for the skills your company requires | proc step, sas base, macro variable, sas enterprise guide, macro facility, sas programmer | source | 13 |
| 56 | Assessing Undergraduate and Postgraduate Hard and Soft Skills in Analytics and Data Science Courses | Richard Self | Traditional approaches to assessing undergraduate assignments in the field of software related courses, including Analytics and Data Science courses, involve the course tutors in reading the students' code and getting the students to physically demonstrate their artefacts. However, this approach tends only to assess the technical skills of solving the set task. It generally fails to assess the many soft skills that industry is looking for, as identified in the e-skills UK (Tech Partnership) / SAS1 report from Nov 2014 and the associated infographic poster. This paper will describe and evaluate the effectiveness of a different approach to defining the assessment task and formatively and summatively assessing the work of the students in order to effectively evaluate and mark both the soft skills, including creativity, curiosity, storytelling, problem solving and communication, as well as the technical skills | data science, data analyst | source | 8 |
| 57 | Automating Load for Multiple Tables in SAS Visual Analytics | Swetha Vuppalanchi | The SAS Visual Analytics autoload feature loads any files placed in the autoload location into the public SAS LASR Analytic Server. But what if we want to autoload the tables into a private SAS LASR Analytic Server and we want to load database tables into SAS Visual Analytics and we also want to reload them every day so that the tables reflect any changes in the database? One o ption is to create a query in SAS Visual Data Builder and schedule the table for load into the SAS LASR Analytic Server, but in this case, a query has to be created for each table that has to be loaded. It would be rather efficient if all tables could be loaded simultaneously; that would make the job a lot easier. This paper provides a custom solution for autoloading the tables in one step, so that tables in SAS Visual Analytics don't have to be reloaded manually or multiple queries don't have to be rerun whenever there is a change in the source data | txt, dataset, sysrc, macro definition | source | 12 |
| 58 | Avoid Change Control by Using Control Tables | Frank A. Ferriola | Developers working on a production process need to think carefully about ways to avoid future changes that require change control, so it's always important to make the code dynamic rather than hardcoding items into the code. Even if you are a seasoned programmer, the hardcoded items might not always be apparent. This paper assists in identifying the harder-to-reach hardcoded items and addresses ways to effectively use control tables within the SAS software tools to deal with sticky areas of coding such as formats, parameters, grouping/hierarchies, and standardization. The paper presents examples of several ways to use the control tables and demonstrates why this usage prevents the need for coding changes. Practical applications are used to illustrate these examples | lookup, macro variable | source | 7 |
| 59 | Bayesian Inference for Gaussian Semiparametric Multilevel Models | Jason Bentley, Cathy Yuen Yi Lee |
Bayesian inference for complex hierarchical models with smoothing splines is typically intractable, requiring approximate inference methods for use in practice. Markov Chain Monte Carlo (MCMC) is the standard method for generating samples from the posterior distribution. However, for large or complex models, MCMC can be computationally intensive, or even infeasible. Mean Field Variational Bayes (MFVB) is a fast deterministic alternative to MCMC. It provides an approximating distribution that has minimum Kullback-Leibler distance to the posterior. Unlike MCMC, MFVB efficiently scales to arbitrarily large and complex models. We derive MFVB algorithms for Gaussian semiparametric multilevel models and implement them in SAS/IML software. To improve speed and mem ory efficiency, we use block decomposition to streamline the estimation of the large sparse covariance matrix. Through a series of simulations and real data examples, we demonstrate that the inference obtained from MFVB is comparable to that of PROC MCMC. We also provide practical demonstrations of how to estimate additional posterior quantities of interest from MFVB either directly or via Monte Carlo simulation |
posterior probability, proc step, monte carlo, proc mixed, sample, regression analysis, bayesian statistics, random intercept, covariance, credible interval, correlation, normal distribution, gamma distribution, fixed effects, data science, proc means | source zip |
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| 60 | Be More Productive! Tips and Tricks to Improve your SAS Programming Environment | Marje Fecht | For me, it's all about avoiding manual effort and repetition! Whether your work involves data exploration, reporting, or analytics, you probably find yourself repeating steps and tasks with each new program, project, or analysis. That repetition adds time to the delivery of results and also contributes to a lack of standardization. This presentation focuses on productivity tips and tricks to help you create a "standard" and efficient environment for your SAS work so you can focus on the results and not the processes. Included are the following: setting up your programming environment (comment blocks, environment cleanup, easy movement between test and production, and modularization) sharing easily with your team (format libra ries, macro libraries, and common code modules) * managing files and results (date and time stamps for logs and output, project IDs, and titles and footnotes) | macro variable, macro facility | source | 6 |
| 61 | Becoming a Unicorn, a Leprechaun, or a Good Marketing Analyst: Fairy Tales Really Can Come True | Emma Warrillow | When you were a kid, did you dress up as a unicorn for Halloween? Did you ever imagine you were a fairy living at the bottom of your garden? Maybe your dreams can come true! Modern-day marketers need a variety of complementary (and sometimes conflicting) skill sets. Forbes and others have started talking about "unicorns", a rare breed of marketer-marketing technologists who understand both marketing and marketing technology. A good marketing analyst is also part of this new breed; a unicorn isn't a horse with a horn but truly a new breed. It is no longer enough to be good at coding in SAS-or a whiz with EXCEL-and to know a few marketing buzzwords. In this paper, we explore the skills an analyst needs to turn himself or herself into one of these mythical, highly sought-after creatures |
data visualization, excel | source | 9 |
| 62 | Best Practices for Configuring Your I/O Subsystem for SAS9 Applications | Tony Brown | The power of SAS9 applications allows information and knowledge creation from very large amounts of data. Analysis that used to consist of 10s to 100s of gigabytes (GBs) of supporting data has rapidly grown into the 10s to 100s of terabytes (TBs). This data expansion has resulted in mor e and larger SAS data stores. Setting up file systems to support these large volumes of data with adequate performance, as well as ensuring adequate storage space for the SAS temporary files, can be very challenging. Technology advancements in storage and system virtualization, flash storage, and hybrid storage management require continual updating of best practices to configure I/O subsystems. This paper presents updated best practices for configuring the I/O subsystem for your SAS9 applications, ensuring adequate capacity, bandwidth, and performance for your SAS9 workloads | note, dataset | source | 11 |
| 63 | Best Practices for Effective Model Risk Management | Satish Garla, Sukhbir Dhillon |
Financial institutions rely heavily on quantitative and qualitative models for risk management, balance- sheet stress testing, and various business analyses and decision support functions. Investment decisions and business strategies are largely driven by estimates computed from such models. Recent financial crises and adverse financial impacts at high-profile banks, attributed variously to model misuse and faulty assumptions, have emphasized the need for a more rigorous governance process over the entire model lifecycle. Regulators have stepped in to assist banks with enhanced guidance and regulations for effective model risk management (MRM). Effective model risk management is more than developing a good model. SAS Model Risk Management provides a robust framework to comprehensively manage an inventory of models including various relationships between them | model risk management, data cleaning, model inventory, model governance, model validation, sample, internal audit | source | 15 |
| 64 | Best Practices for Machine Learning Applications | Brett Wujek, Funda Güneş, Patrick Hall |
Building representative machine learning models that generalize well on future data requires careful consideration both of the data at hand and of assumptions about the various available training algorithms. Data are rarely in an ideal form that enables algorithms to train effectively. Some algor ithms are designed to account for important considerations such as variable selection and handling of missing values, whereas other algorithms require additional preprocessing of the data or appropriate tweaking of the algorithm options. Ultimate evaluation of a model's quality requires appropriate selection and interpretation of an assessment criterion that is meaningful for the given problem. This paper discusses many of the most common issues faced by machine learning practitioners and provides guidance for using these powerful algorithms to build effective models | missing value, feature extraction, outlier | source | 23 |
| 65 | Best Practices for Resource Management in Hadoop | James Kochuba | SAS solutions that run in Hadoop provide you with the best tools to transform data in Hadoop. They also provide insights to help you make the right decisions for your business. It is possible to incorporate SAS products and solutions into your shared Hadoop cluster in a cooperative fashion with YARN to manage the resources. Best practices and customer examples are provided to show how to build and manage a shared cluster with SAS applications and products | sas grid, logistic regression, grid computing, proc hplogistic, logistic distribution, regression analysis, proc sql, libname statement | source | 10 |
| 66 | Best Practices in Connecting External Databases to SAS | Mathieu Gaouette | Connecting database schemas to libraries in the SAS metadata is a very important part of setting up a functional and useful environment for business users. This task can be quite difficult for the untrained administrator. This paper addresses the key configuration items that often go unnoticed but can make a big difference. Using the wrong options can lead to poor database performance or even to a total lockdown, depending on the number of connections to the database | sas management console, sas enterprise guide, proc sql | source | 7 |
| 67 | Beyond Best Practice: Grid Computing in the Modern World | Jan Bigalke, Gregory S. Barnes Nelson |
data warehouse, grid computing | source | 17 | |
| 68 | Big Data in Higher Education: From Concept to Results | Stephanie R. Thompson | Higher education is taking on big data initiatives to help improve student outcomes and operational efficiencies. This paper shows how one institution went form White Paper to action. Challenges such as developing a common definition of big data, access to certain types of data, and bringing together institutional groups who had little previous contact are highlighted. The stepwise, collabora tive approach taken is highlighted. How the data was eventually brought together and analyzed to generate actionable results is also covered. Learn how to make big data a part of your analytics toolbox | big data, proof of concept, data governance | source | 4 |
| 69 | Big Data, Big Headaches: An Agile Modeling Solution Designed for the Information Age | Mariam Seirafi | The surge of data and data sources in marketing has created an analytical bottleneck in most organizations. Analytics departments have been pushed into a difficult decision: either purchase "black - box" analytical tools to generate efficiencies or hire more analysts, modelers, and data scientists. Knowledge gaps stemming from restrictions in black-box tools or from backlogs in the work of analytical teams have resulted in lost business opportunities. Existing big data analytics tools respond well when dealing with large record counts and small variable counts, but they fall short in bringing efficiencies when dealing with wide data. This paper discusses the importance of an agile modeling engine designed to deliver productivity, irrespective of the size of the data or the complexity of the modeling approach | big data, excel, dataset, sample, proc varreduce | source | 13 |
| 70 | Breakthroughs at Old Dominion Electric Cooperative with Energy Load Forecasting Innovation | David Hamilton, Steve Becker, Emily Forney |
As the electrical grid has become more complex, utilities are revisiting their approaches, methods, and technology to accurately predict energy demands across all time horizons in a timely manner. With the advanced analytics of SAS Energy Forecasting, ODEC provides data-driven load predictions from next hour to next year and beyond. Accurate intraday forecasts mean meeting daily peak demands saving millions of dollars at critical seasons and events. Mid-term forecasts provide a baseline to the cooperative and its members to accurately anticipate regional growth and customer needs in addition to signaling power marketers where, when and how much to hedge future energy purchases to meet weather -driven demands. Long-term forecasts create defensible numbers for large capital expenditures such as generation and transmission projects | time series analysis, proc step, champion model, proc sgscatter, sample, proc sgpanel, predictor variable, outlier | source | 19 |
| 71 | Bridging the Gap: Importing Health Indicators Warehouse Data into SAS Visual Analytics Using SAS Stored Processes and APIs | Li-Hui Chen, Manuel Figallo-Monge |
The Health Indicators Warehouse (HIW) is part of the US Department of Health and Human Services' (DHHS) response to make federal data more accessible. Through it, users can access data and metadata for over 1,200 indicators from approximately 180 federal and nonfederal sources. The HIW also supports data access by applications such as SAS Visual Analytics through the use of an application programming interface (API). An API serves as a communication interface for the integration of two separate systems. As a result of the API, HIW data consumers using SAS Visual Analytics can avoid difficult manual data processing. This paper provides detailed information about how to access HIW data with SAS Visual Analytics in order to produce easily understood visualizations with minimal effort through a methodology that automates HIW data processing | macro definition, api, sample, data governance | source | 12 |
| 72 | Bringing Google Analytics, Facebook, and Twitter Data to SAS Visual Analytics | I-Kong B. Fu, Mark Chaves, Andrew Fagan |
Your marketing team would like to pull data from its different marketing activities into one report. What happens in Vegas might stay in Vegas, but what happens in your data does not have to stay there, locked in different tools or static spreadsheets. This paper demonstrates how to easily bring data from Google Analytics, Facebook, and Twitter into SAS Visual Analytics in order to create interactive explorations and reports on these data along with your other data for better overall understanding of your marketing effectiveness | customer experience, web analytics | source | 12 |
| 73 | Bringing the US Department of Defense from PC to the Enterprise! | Erin Stevens, Douglas B. Liming |
A US Department of Defense (DoD) agency with over USD 40 billion in sales and revenue, 25,000 employees, and 5.3 million parts to source, partnered with SAS to turn its disparate PC-based analytic environment into a modern SAS Grid Computing server-based architecture. This presentation discusses the challenges of under-powered desktops, data sprawl, outdated software, difficult upgrades, and inefficient compute processing, and the solution crafted to enable the agency to run as the Fortune 50 company that its balance sheet (and our nation's security) demand. In the modern architecture, rolling upgrades, high availability, centralized data storage, and improved performance enable more accurate forecasting, which means getting our troops the supplies that they need-when and where they need them | sas grid, grid computing, inventory optimization, analytics platform, data warehouse, excel | source | 5 |
| 74 | Building a Recommender System with SAS to Improve Cross-Selling for Online Retailers | Miao Nie, Shanshan Cong |
Nowadays, the recommender system is a popular tool for online retailer business to predict the customers' next-product-to-buy (NPTB). Based on the information collected by the retailer and statistical techniques, an efficient recommender system can suggest customers with meaningful NPTB. A useful suggestion can reduce customer's searching time for a wanted product and improve buying experience, which would increase the chance of cross-selling for online retailers, helping them build customer loyalty. Within a recommender system, how to combine advanced statistical techniques with information available (customer profiles, product attributes, good selling products, etc.) is the key part to bridge the retailer's database with the useful suggestion of NPTB for c ustomers. This paper illustrates how to create a recommender system with SAS RECOMMEND procedure for online business |
proc hpsplit, logistic regression, response variable, recommendation engine, regression analysis, predictor variable | source zip |
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| 75 | Building and Using User Defined Formats | Arthur L. Carpenter | Formats are powerful tools within the SAS System. They can be used to change how information is brought into SAS, how it is displayed, and can even be used to reshape the data itself. The Base SAS product comes with a great many predefined formats and it is even possible for you to create your own specialized formats. This paper will very briefly review the use of formats in general and will then cover a number of aspects dealing with user generated formats. Since formats themselves have a number of uses that are not at first apparent to the new user, we will also look at some of the broader application of formats. Topics include; building formats from data sets, using picture formats, transformations using formats, value translations, and using formats to perform table look-ups | proc format, proc step, char., character variable, put statement, where statement, sysdate9, sas programmer, proc tabulate | source zip |
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| 76 | Building Interactive Microsoft Excel Worksheets with SAS Office Analytics | Tim Beese | Microsoft Office has over 1 billion users worldwide, making it one of the most successful pieces of software on the market today. Imagine combining the familiarity and functionality of Microsoft Office with the power of SAS to include SAS content in a Microsoft Office document. By using SAS Office Analytics, you can create Microsoft EXCEL worksheets that are not just static reports, but interactive documents. This paper shows how to create an interactive experience in EXCEL by leveraging Visual Basic for Applications using SAS data and stored processes. Finally, this paper demonstrates how to open SAS Visual Analytics reports into EXCEL, so the interactive benefits of SAS Visual Analytics are combined with the familiar interface of an EXCEL worksheet. All these interactions with SAS content are possible without leaving Microsoft EXCEL |
excel, macro variable, sas enterprise guide, xls, proc sql | source | 20 |
| 77 | Building Macros for Quick Survey Scoring | Vincent Chan, Lorena Ortiz |
Surveys are a critical research component when gathering information about a population of people. Researchers often implement surveys after a population has participated in a class or educational program with the aim to capture knowledge, attitudes, and experiences of participants. Survey developers create surveys using psychometric principles to ensure the reliability and validity of each question, also known as an 'item.' A developer can purposefully create sets of items, which when analyzed as a group, can measure a construct that describes the underlying behavior, attribute, or characteristic of a study participant. The programmer calculates the construct score by combining (e.g. summing or averaging) all component items of the construct. The programmer is tasked with creating such scores quickly. With an unlimited amount of time to spend, a programmer could operationalize the creation of these constructs by manually entering predefined formulas in the DATA STEP |
macro variable, proc sql, macro loop | source | 7 |
| 78 | By the Docs: Securing SAS Software | Robin Crumpton, Donna Bennett, Qiana Eaglin |
Have you ever wondered the best way to secure SAS software? Many pieces need to be secured-from passwords and authentication to encryption of data at rest and in transit. In this paper we discuss several security tasks that are important when setting up SAS, and we provide some tips for findin g the information that you need in the mountains of SAS documentation. The tasks include 1) enabling basic network security (TLS) using the automation options in the latest release of SAS 9.4; 2) configuring HTTPS for the SAS middle tier (once the TLS connection is established); 3) setting up user accounts and IDs with an eye toward auditing user activity. Whether you are an IT expert who is concerned about corporate security policies, a SAS Administrator who needs to be able to describe SAS capabilities a nd configure SAS software to work with corporate IT standards, or an auditor who needs to review specific questions about security vulnerabilities, this paper is for you | data mart, error message, sas management console, sas administrator, options statement | source | 17 |
| 79 | Can you decipher the code? If you can, maybe you can break it | Jayanth Iyengar | You would think that training as a code breaker, similar to those employed during the Second World War, wouldn't be necessary to perform routine SAS programming duties of your job, such as debugging code. However, if the author of the code doesn't incorporate good elements of style in their program, the task of reviewing code becomes arduous and tedious for others. Style touches upon very specific aspects of writing code; indentation for code and comments, case for keywords, variables, and data set names, and spacing between PROC and DATA steps. Paying attention to these specific issues will enhance the reusability and lifespan of your code. By using style to make your code readable, you'll impress your superiors and grow as a programmer |
sample, proc step, dataset | source | 7 |
| 80 | Can You Read This into SAS for Me? Using INFILE and INPUT to Load Data into SAS | Peter W. Eberhardt, Audrey Yeo |
With all the talk of "Big Data" and "Visual Analytics" we sometimes forget how important, and often hard, it is to get external data into SAS. In this paper we will review some common data sources such as delimited sources (e.g. CSV) as well as structured flat files and the programming steps needed to successfully load these files into SAS. In addition to examining the INFILE and INPUT statements, we will look at some methods for dealing with bad data. This paper assumes only basic SAS skills, although the topic can be of interest to anyone who needs to read external files |
input statement, infile statement, raw data, trailing @, missing value, date variable, character variable, flat file, dataset, length statement | source | 20 |
| 81 | Can You Stop the Guesswork in Your Marketing Budget Allocation? Marketing Mixed Modeling Using SAS Can Help! | Delali Agbenyegah | Even though marketing is inevitable in every business, each and every year the marketing budget is limited and prudent fund allocations are required to optimize marketing investment . In many businesses, the marketing fund is allocated based on the marketing manager's experience, departmental budget allocation rules and sometimes 'gut feelings' of business leaders. Those traditional ways of budget allocation yield sub optimal results and in many cases lead to money wasting on certain irrelevant marketing efforts. Market Mixed models can be used to understand the effects of marketing activities and identify the ke y marketing efforts that drive the most sales among a grou p of competing marketing activities. The results can be used in marketing budget allocation and take out the guess work that typically goes into the budget allocation. In this paper, we illustrate how t o develop and implement Market Mixed Modeling using SAS procedures from a practical perspective | marketing mix, data science, proc mixed, model training, sample, estimate, regression analysis, unit root, proc reg, predictor variable | source | 7 |
| 82 | Careers in Biotatistics and Clinical SAS Programming: An Overview for the Uninitiated | Justina M. Flavin | In the biopharmaceutical industry, biostatistics plays an important and essential role in the research and development of drugs, diagnostics, and medical devices. Familiarity with biostatistics combined with knowledge of SAS can lead to a challenging and rewarding career that also positively impacts and transforms patients' lives. This paper will provide a broad overview of the different types of jobs and career paths available, discuss the education and skill sets needed for each, and present some ideas for overcoming entry barriers into careers in biostatistics and clinical SAS programming | clinical trial, sas programming, clinical data, data science, pharma programming, csr, clinical research, clinical sas, biostatistician, statistical software, macro definition, proc sql | source | 5 |
| 83 | Carry-On Suitcases and Mobile Devices: Using SAS Visual Analytics Designer for Creating Optimally Designed Reports for SAS Mobile BI | Lavanya Mandavilli, Anand V. Chitale |
Packing a carry-on suitcase for air travel and designing a report for mobile devices have a lot in common. Your carry-on suitcase contains indispensable items for your journey, and the contents are limited by tight space. Your reports for mobile devices face similar challenges-data display is governed by tight real estate space and other factors such as users' shorter attention span and information density come into play. How do you overcome these challenges while displaying data effectively for your mobile users? This paper demonstrates how smaller real estate on mobile devices, as well as device orientation in portrait or landscape mode, influences best practices for designing reports. The use of containers, layouts, filters, information windows, and carefully selected objects enable you to design and guide user interaction effectively. Appropriate selection of font styles, font sizes, and colors reduce distraction and enhance quick user comprehension | business intelligence | source | 20 |
| 84 | Case Study Using SAS Forecast Studio: Daily Taxi Data from New York City | Simon Sheather | Presentation | time series analysis, feature selection, sample, residual, outlier, scenario analysis, missing value | source | 47 |
| 85 | Case Study: SAS Visual Analytics Dashboard for Pollution Analysis | Viraj R Kumbhakarna | This paper is a case study to explore the analytical and reporting capabilities of SAS Visual Analytics to perform data exploration, determine order patterns and trends, and create data visualizations to generate extensive dashboard reports using the open source pollution data available from the US Environmental States Environment Protection Agency. Data collection agencies report their data to EPA via the system called Air Quality System (AQS) and it makes available several types of aggregate (summary) datasets such as daily and annual pollutant summaries in CSV format for public use. We intend to demonstrate SAS Visual Analytics capabilities by using pollution data to create visualizations which compare Air Quality Index (AQI) values for multiple pollutants by location and time period, generate a time series plot by location and time period, compare 8-hour ozone "exceedances" from this year with previous years and perform other such analysis |
time series analysis, series plot, sample, raw data, data visualization, dataset, proc sort | source | 12 |
| 86 | Cell Suppression In SAS Visual Analytics: A Primer | Marc Flore | In healthcare and other fields, the importance of cell suppression as a means to avoid unintended disclosure or identification of Protected Health Information (PHI) or any other sensitive data has grown as we move toward dynamic query systems and reports. Organizations such as the Centers for Medicare & Medicaid Services (CMS), the National Center for Health Statistics (NCHS), and the Privacy Technical Assistance Center (PTAC) have outlined best practices to help researchers, analysts, report writers, and others avoid unintended disclosure for privacy reasons and to maintain statistical validity. Cell suppression is a crucial consideration during report design and can be a substantial hurdle in the dissemination of information. Often, the goal is to display as much data as possible without enabling the identification of individuals and while maintaining statistical validity. When designing reports using SAS Visual Analytics, achieving suppression can be handled multiple ways | sample, sas base | source | 7 |
| 87 | Change Management: The Secret to a Successful SAS Implementation | Gregory S. Barnes Nelson | Whether you are deploying a new capability with SAS or modernizing the tool set that people already use in your organization, change management is a valuable practice. Sharing the news of a change with employees can be a daunting task and is often put off until the last possible second. Organizations frequently underestimate the impact of the change, and the results of that miscalculation can be disastrous. Too often, employees find out about a change just before mandatory training and are expected to embrace it. But change management is far more than training. It is early and frequent communication, an inclusive discussion, encouraging and enabling the development of an individual, and facilitating learning before, during, and long after the change. This paper not only showcases the importance of change management but also identifies key objectives for a purposeful strategy. We outline our experiences with both successful and not so successful organizational changes | source | 20 | |
| 88 | Classifying Fatal Automobile Accidents in the US, 2010-2013: Using SAS Enterprise Miner to Understand and Reduce Fatalities | Catherine LaChapelle, Daniel Brannock, Aric LaBarr, Craig Shaver |
proc hpsplit, response variable, data mining, validation set, proof of concept, data cleaning, sample, key | source | 11 | |
| 89 | Cleaning Up Your SAS Log: Note Messages | Jennifer M. Srivastava | As a SAS programmer, you probably spend some of your time reading and possibly creating specifications. Your job also includes writing and testing SAS code to produce the final product, whether it is SDTM datasets, ADaM datasets or statistical outputs such as tables, listings or figures. You reach the point where you have completed the initial programming, removed all obvious errors and warnings from your SAS log and checked your outputs for accuracy. You are almost done with your programming task, but one important step remains. It is considered best practice to check your SAS log for any questionable messages generated by the SAS system. In addition to messages that begin with the words WARNING or ERROR, there are also messages that begin with the words NOTE or INFO. This paper will focus on five different types of NOTE messages that commonly appear in the SAS log and will present ways to remove these messages from your log | sas log, missing value, cdisc | source | 8 |
| 90 | Clinical Graphs Using SAS | Sanjay N. Matange | Graphs are essential for many clinical and health care domains, including analysis of clinical trials safety data and analysis of the efficacy of the treatment, such as change in tumor size. Creating such graphs is a breeze with procedures from SAS 9.4 ODS Graphics. This paper shows how to create many industry - standard graphs such as Lipid Profile, Swimmer Plot, S urvival Plot, Forest Plot with Subgroups, Waterfall Plot, and Patient Profile using Study Data Tabulation Model (SDTM) data |
proc sgplot, kaplan-meier plot, forest plot, swimmer plot, adverse events, scatter plot, hazard ratio, waterfall chart, box plot, confidence interval, proc step, cdisc | source zip |
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| 91 | College Football Can the Public Predict Games Correctly? | Matthew B. Collins, Taylor K. Larkin |
Presentation | source | 6 | |
| 92 | Color, Rank, Count, Name; Controlling it all in PROC REPORT | Arthur L. Carpenter | Managing and coordinating various aspects of a report can be challenging. This is especially true when the structure and composition of the report is data driven. For complex reports the inclusion of attributes such as color, labeling, and the ordering of items complicates the coding process. Fortunately we have some powerful reporting tools in SAS that allow the process to be automated to a great extent. In the example presented in this paper we are tasked with generating an EXCEL spreadsheet that ranks types of injuries within age groups. A given injury type is to receive a constant color regardless of its rank and the labeling is to include not only the injury label, but the actual count as well. Of course the user needs to be able to control such things as the age groups, color selection and order, and number of desired ranks |
proc step, proc report, proc format, excel, proc rank | source | 9 |
| 93 | Combining the power of SAS Tools (SAS Data Integration Studio, SAS/OR Software, SAS Visual Analytics, and Base SAS) | Wouter Travers | Traditionally, policy makers have made policy choices based on experience, political consensus building and sporadic research. Today, when faced with policy choices, the use of automated decision-making systems is becoming less controversial. However, when computer systems are used to assist in making policy choices, it is essential to use objective data to support the decision-making process. By using a combination of SAS products, we provide insights to our client on how they can optimize the location of fire stations, which is a very impactful policy choice. SAS offers a very divers number of products covering the full ecosystem needed to provide insights to our clients in one integrated platform. We exploited specific catalyzing factors of SAS products throughout the ecosystem. SAS Data integration studio for data management, SAS Visual Analytics as a visualization tool and SAS Enterprise guide, SAS/STAT and SAS/OR as the Analytics component |
sas base, sas enterprise guide | source | 10 |
| 94 | Comparative Regional Analysis of Bacterial Pneumonia Readmission Patients in the Medicare Category | Heramb Joshi, Goutam Chakraborty, William Paiva, Aditya Sharma |
Recently hospital re-admissions have become a major growing concern in the healthcare industry. It causes excessive utilization of medical resources, time, monetary aspects and above all patient dissatisfaction. Excessive re-admissions also result in penalties incurred by hospitals. Hospital re- admission is defined as more than one inpatient visit made by an individual for the same diagnosis in a short span of time. It is identified as 30 days by Medicare in United States. This paper aims at the analysis of hospital re-admissions to identify diagnostic procedural anomalies and recognize preventive measures to benefit patients, hospitals, government and insurance companies | logistic regression, regression analysis, logistic distribution, estimate, stepwise selection, backward selection, odds ratio, data cleaning | source | 12 |
| 95 | Comparative Study of PROC EXPORT and Output Delivery System | Saurabh Nandy | Imagine that you have a very large dataset and you have some specific values in one of the columns of the dataset and you want to classify the entire dataset into different CSV sheets based on the values present in that specific column. Perhaps you think you will use codes using IF/THEN and ELSE statement conditions in SAS along with some OUTPUT statements. Considering the fact that you are thinking to divide that dataset into CSV sheets, it kind of makes it more frustrating to do that using the conventional manual process of converting each of the separated datasets into CSV files. This paper looks at a comparative study of using the Macro command in SAS with the help of PROC EXPORT statement and ODS command using PROC TABULATE. In these two processes, the whole tedious process is done automatically using the SAS code |
proc step, proc export, excel | source | 9 |
| 96 | Competing-Risks Analyses: An Overview of Regression Models | Joseph C. Gardiner | Competing-risks analyses are methods for analyzing the time to a terminal event (such as death or failure) and its cause or type. The cumulative incidence function CIF(j, t) is the probability of death by time t from cause j. New options in the LIFETEST procedure provide for nonparametric estimation of the CIF from event times and their associated causes, allowing for right-censoring when the event and its cause are not observed. Cause-specific hazard functions that are derived from the CIFs are the analogs of the hazard function when only a single cause is present. Death by one cause precludes occurrence of death by any other cause, because an individual can die only once. Incorporating explanatory variables in hazard functions provides an approach to assessing their impact on overall survival and on the CIF. This semiparametric approach can be analyzed in the PHREG procedure |
competing risks, cause-specific hazard, likelihood function, regression analysis, hazard function, survival function, proc step, hazard ratio, right censoring, confidence interval, survival analysis, fine-gray model, sample, proc phreg, proc calis, kaplan-meier estimator, censoring, cumulative hazard | source | 22 |
| 97 | Considerations in Organizing the Structure of SAS Macro Libraries | Roger D. Muller | SAS macros offer a very flexible way of developing, organizing, and running SAS code. In many systems, programming components are stored as macros in files and called as macros via a variety of means so that they can perform the task at hand. Consideration must be given to the organization of these files in a manner consistent with proper use and retention. An example might be the development of some macros that are company-wide in potential application, others that are departmental, and still others that might be for personal or individual user. Super-imposed on this structure in a factorial fashion are the need to have areas that are considered: (1) production - validated, (2) archived - retired and (3) developmental. Several proposals for accomplishing this are discussed as well as how this structure might interrelate with stored processes available in some SAS systems | macro definition, macro variable | source presentation |
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| 98 | Controlling Colors by Name; Selecting, Ordering, and Using Colors for Your Viewing Pleasure | Arthur L. Carpenter | Within SAS literally millions of colors are available for use in our charts, graphs, and reports. We can name these colors using techniques which include color wheels, RGB (Red, Green, Blue) HEX codes, and HLS (Hue, Lightness, Saturation) HEX codes. But sometimes I just want to use a color by name. When I want purple, I want to be able to ask for purple not CX703070 or H03C5066. But am I limiting myself to just one purple? What about light purple or pinkish purple. Do those colors have names or must I use the codes? It turns out that they do have names. Names that we can use. Names that we can select, names that we can order, names that we can use to build our graphs and reports. This paper will show you how to gather color names and manipulate them so that you can take advantage of your favorite purple; be it 'purple', 'grayish purple', 'vivid purple', or 'pale purplish blue'. Much of the control will be obtained through the use of user defined formats | proc step, proc format, txt, dataset, proc report, excel, proc sql | source | 11 |
| 99 | Correcting the Quasi-complete Separation Issue in Logistic Regression Models | Xinghe Lu | Quasi-complete separation is a commonly detected issue in logit/probit models. Quasi -complete separation occurs when the dependent variable separates an independent variable or a combination of several independent variables to a certain degree. In other wo rds, levels in a categorical variable or values in numeric variable are separated by groups in a discrete outcome variable. Most of the time, it happens in categorical independent variable(s). Quasi -complete separation can cause convergence failures in the model, which consequently result in inflated coefficient estimate s and standard errors. Therefore it potentially yields biased results. This paper reviews various approaches for correcting Quasi -complete separation using a binary logistic regression model as the example . First it introduces the concept of Quasi -complete separation and how to diagnose the issue | logistic regression, logistic distribution, regression analysis, predictor variable, sample, nominal variable, response variable, proc calis, proc freq, likelihood function, proc step, proc logistic, sample size, maximum likelihood estimation | source | 10 |
| 100 | Create Web-Based SAS Reports Without Having to Be a Web Developer | Kris Kiser, Christie Corcoran, Marie Dexter, Amy Peters |
You might already know that SAS Studio tasks provide you with prompts to "fill in the blanks" in SAS code and help you navigate SAS syntax. But did you know that SAS Studio tasks are not only designed to allow you to modify them, but that there's an entire common task model (CTM) provided for you to build your own? You can build basic utilities or complex reports. You can just run SAS code or you can have elaborate prompts to dynamically generate code. And since SAS Studio runs in a web browser, your tasks are browser-based and can be easily shared with others. In this paper you will learn how to take a typical SAS program and create a SAS Studio task from it. When the task is run, you'll see web-based prompts for the dynamic portions of the program and HTML output delivered to your browser | sas studio, create table, proc step, macro variable | source | 15 |
| 101 | Creating a better SAS Visual Analytics user experience under HIPAA data restrictions | Margaret Burgess, Catherine Olson, Jessica Wegner |
Presentation | source | 6 | |
| 102 | Creating a Q-gram Algorithm to Determine the Similarity of Two Character Strings | Joe DeShon | This paper shows how to program a powerful Q-gram algorithm for measuring the similarity of two character strings. Along with built-in SAS functions -- such as SOUNDEX, SPEDIS, COMPGED, and COMPLEV -- Q-gram can be a valuable tool in your arsenal of string comparators. Q-gram is a method to evaluate letter patterns in words; pairs of words with a high Q-gram score have a large number of similar letter patterns, which is a good measure of their similarity. Q-gram is especially useful when measuring the similarity of strings that are intuitively identical, but which have a different word order, such as "John Smith" and "Smith, John" | sas base, data wrangling, likelihood function, proc sql | source | 7 |
| 103 | Creating a Strong Business Case for SAS Grid Manager: Translating Grid Computing Benefits to Business Benefits | Marlos Bosso | SAS Grid Manager, as well as other grid computing technologies, have a set of great capabilities that we, IT professionals, love to have in our systems. This technology increases high availability, allows parallel processing, facilitates increasing demand by scale out, and offers other features that make life better for those managing and using these environments. However, even when business users take advantage of these features, they are more concerned about the business part of the problem. Most of the time business groups hold the budgets and are key stakeholders for any SAS Grid Manager project. Therefore, it is crucial to demonstrate to business users how they will benefit from the new technologies, how the features will improve their daily operations, help them be more efficient and productive, and h elp them achieve better results | sas grid, grid computing, parallel processing | source | 10 |
| 104 | Creating Amazing Visualisations with SAS Stored Processes and JavaScript libraries | Philip A. Mason | macro variable, proc step, sas enterprise guide, excel, dataset, proc json, sample, proc sql | source | 20 | |
| 105 | Creating and Sharing SAS ODS Graphics with a Code Playground Based on Microsoft Office | Ted Conway, Ezequiel Torres |
You've heard that SAS Output Delivery System (ODS) Graphics provides a powerful and detailed syntax for creating custom graphs, but for whatever reason you still haven't added them to your bag of SAS tricks. Let's change that! We will also present a code playground based on Microsoft Office that will enable you to quickly try out a variety of prepared SAS ODS Graphics examples, tweak the code, and see the results-all directly from Microsoft EXCEL. More experienced users will also find the code playground (which is similar in spirit to Google Code Playground or JSFiddle) useful for compiling SAS ODS Graphics code snippets for themselves and for sharing with colleagues, as well as for creating dashboards hosted by Microsoft EXCEL or Microsoft PowerPoint that contain precisely sized and placed SAS graphics |
excel, sas studio, xls, output delivery system | source | 4 |
| 106 | Credit Card Interest and Transactional Income Prediction with an Account-Level Transition Panel Data Model Using SAS/STAT and SAS/ETS Software | Denys Osipenko | Credit card profitabili ty prediction is a complex problem because of the variety of card holders' behavior patterns and the different sources of interest and transactional income. Each consumer account can move to a number of states such as inactive, transactor, revolver, delinq uent, or defaulted. This paper i) describes an approach to credit card account -level profitability estimation based on the multistate and multistage conditional probabilities models and different types of income estimation, and ii) compares methods for the most efficient and accurate estimation. We use application, behavioral, card state dynamics, and macroeconomic characteristics, and their combinations as predictors. We use different types of logistic regression such as multinomial logistic regression, or dered logistic regression, and multistage conditional binary logistic regression with the LOGISTIC procedure for states transition probability estimation |
regression analysis, logistic regression, logistic distribution, credit card, interest, proc mixed, multinomial logistic regression, sample, fee income, conditional logistic regression, panel data, lgd, expected loss, time series analysis, pd, data cleaning, proc hpsplit, proc logistic, delinquency, interest rate, stage 2, proc step, proc reg | source | 20 |
| 107 | Credit Risk Modeling in a New Era | Wei Chen, Martim Rocha, Jimmy Skoglund |
The recent advances in regulatory stress testing, including stress testing regulated by Comprehensive Capital Analysis and Review (CCAR) in the US, the Prudential Regulation Authority (PRA) in the UK, and the European Banking Authority in the EU, as well as the new international accounting requirement known as IFRS 9 (International Financial Reporting Standard), all pose new challenges to credit risk modeling. There is an increasingly sophisticated requirement on credit models to cover long-term survival projections as well as integration of economic forward-looking components. Models are also required at a more granular level. The fact that credit models are supposed to cover all the material risks in the underlying assets in various economic scenarios makes the models harder to implement | transition matrix, stress testing, credit risk, stochastic economic scenario, credit risk analyst, expected loss, sample, economic capital, macro-economic scenario, proc phreg, credit portfolio, logistic distribution, regression analysis, ifrs 9, cash flow forecast, pd, best estimate liability, capital adequacy, competing risks, credit score, logistic regression, markov chain, credit migration, sensitivity analysis | source | 16 |
| 108 | Custom Risk Metrics with SAS High-Performance Risk | Katherine Taylor, Steven Miles |
There are standard risk metrics financial institutions use to assess the risk of a portfolio. These include well-known measures like value at risk and expected shortfall and many more. While there are industry - standard approaches for calculating these measures, it is often the case that financial institutions have their own methodologies. Further, financial institutions sometimes write their own measures. SAS High- Performance Risk comes equipped with over twenty risk measures that use standard methodology, but the product also allows customers to define their own risk measures. This paper leads the user through the creation of custom risk metrics using the HPRISK procedure |
variance, expected loss, expected shortfall, proc reg | source | 6 |
| 109 | Customer Lifetime Value Modeling | Vadim Pliner | Customer lifetime value (LTV) estimation involves two parts: the "survival" probabilities and profit margins. This article describes the estimation of those probabilities using discrete-time logistic hazard models and that of profit margins is based on linear regression. In the scenario when outliers are present among margins, we suggest applying robust regression with PROC ROBUSTREG |
logistic distribution, regression analysis, proc step, survival analysis, proc logistic, outlier, hazard function, customer lifetime value, proc reg, proc robustreg, time-dependent covariate, likelihood function, predictor variable | source presentation |
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| 110 | Cyclist Death and Distracted Driving: Important Factors to Consider | Lysbeth Floden, Melanie Bell, Patrick O'Connor |
sample, data cleaning, likelihood function, sample size | source | 12 | |
| 111 | Data Analysis with User-Written DS2 Packages | Robert S. Ray, William Eason |
The DATA STEP and DS2 both offer the user a built-in general purpose hash object that has become the go-to tool for many data analysis problems. However, there are occasions where th e best solution would require an object specifically customized for the problem space. The DS2 package syntax enables the user to create objects that can be linked to form structures in memory such as trees or lists that can then be traversed and manipulated. For data that can describe many more states than actually exist, dynamic structures such as this can provide a compact way to represent and analyze the data. The SAS In- Database Code Accelerator enables these custom packages to be deployed in parallel on massive data grids. Further, with the use of the SAS Macro facility, it is possible to create a package template that facilitates code reuse in a strongly typed system |
proc freq, macro definition, by group processing, hash table, proc step, macro facility | source | 11 |
| 112 | Data Review Listings on Auto-Pilot: Using SAS and Windows Server to Automate Reports and Flag Incremental Data Records | Victor Lopez, Heli Ghandehari, Bill Knowlton, Christopher J. Swearingen |
During the course of a clinical trial study, large numbers of new and modified data records are received on an ongoing basis. Providing end users with an approach to continuously review and monitor study data, while enabling them to focus reviews on new or modified (incremental) data records, allows for greater efficiency in identifying potential data issues. In addition, supplying data reviewers with a familiar machine-readable output format (for example, Microsoft EXCEL) allows for greater flexibility in filtering, highlighting, and retention of data reviewers' comments. In this paper, we outline an approach using SAS in a Windows server environment and a shared folder structure to automatically refresh data review listings. Upon each execution, the listings are compared against previously reviewed data to flag new and modified records, as well as carry forward any data reviewers' comments made during the previous review. In addition, we highlight the use and capabilities of the SAS ExcelXP tagset, which enables greater control over data formatting, including management of Microsoft EXCEL's sometimes undesired automatic formatting. Overall, this approach provides a significantly improved end-user experience above and beyond the more traditional approach of performing cumulative or incremental data reviews using PDF listings |
excel, data review, clinical trial | source | 12 |
| 113 | Data to Dashboard: Visualizing Classroom Utilization and Diversity Trends with SAS Visual Analytics | Shweta Doshi, Julie Davis |
Transforming data into intelligence for effective decision-making support is critically based on Office of Institutional Research's role and capacity in managing the institution's data. Presenters will share their journey from providing spreadsheet data into developing SAS programs and dashboards using SAS Visual Analytics. Experience gained and lessons learned will also be shared at this session. The presenters will: 1. demonstrate two dashboards the IR office developed, one for classroom utilization and one for the University's diversity initiatives; 2. share the steps for creating the dashboards; 3. describe the process the office took in ge tting the stakeholders involved in determining the KPIs, evaluating and providing feedback regarding the dashboard; and 4. share their experience gained and lessons learned in building the dashboard; | dataset, data wrangling, excel, sas enterprise guide, sas visual analytics | source | 11 |
| 114 | Dealing with Nanoseconds in SAS Datetime Values in Transaction Processing | Richard D. Langston | This paper describes a technique for dealing with the precision problems inherent in DATETIME values containing nanosecond data. Floating-point values cannot store sufficient precision for this, and this limitation can be a problem for transactional data where nanoseconds are pertinent. Methods discussed include separation of variables and using the special GROUPFORMAT feature of the BY statement with MERGE in the DATA STEP |
proc step, by group processing | source | 4 |
| 115 | Debt Collection through the Lens of SAS Analytics | Karush Jaggi, Harold Dickerson, Thomas Waldschmidt |
Debt Collection! The two words can trigger multiple images in one's mind - mostly harsh. However, let's try and think positively for a moment. In 2013, over $55 billion of past due debt was recovered by agencies in the United States. What if all of these debts were left as is and the fate of credit issuers in the hands of good will payments made by defaulters? Well, not the most sustainable model to say the least. In this situation, debt collection comes in as a tool that is employed at multiple levels of recovery to keep the credit flowing. Ranging from in-house to third party to individual collection efforts, this industry is huge and plays an important role in keeping the engine of commerce running. In the recent past, with financial markets recovering and banks selling less of charged off accounts and at higher prices, debt collection has increasingly become a game of efficient operations backed by solid analytics | proc hpsplit, debt collection, regression analysis, data mining, ks statistic, imputation, feature importance | source presentation |
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| 116 | Deep Dive with SAS Studio into SAS Grid Manager 9.4 | Edoardo Riva | Do you know how many different ways SAS Studio can run your programs with SAS Grid Manager? SAS Studio is the latest and coolest interface to SAS software. As such, we want to use it in most situations, including with sites that leverage SAS Grid Manager. Are you new to SAS and want to be guided by a modern UI? SAS Studio is here to help you. Are you a code fanatic, who wants total control of how your program runs to harness the full power of SAS Grid Manager? Sure, SAS Studio is for you, too. This paper covers all SAS Studio editions. You learn how to connect each of them to SAS Grid Manager and discover best practices for harnessing a high-performance SAS analytics environment, while avoiding potential pitfalls | sas studio, sas grid, work library, parallel processing, grid computing | source | 15 |
| 117 | Design for Success: An Approach to Metadata Architecture for Distributed Visual Analytics | Ratul Saha, Vimal Raj, Vignesh Balasubramanian |
Metadata is an integral and critical part of any environment. Metadata facilitates in resource discovery and provides a unique identification of every single digital component of a system, simple to complex. SAS Visual Analytics being one of the most powerful analytics visualization platform, leverages the power of metadata to provide a plethora of functionalities for all types of users. The possibilities range from real-time advanced analytics and power-user reporting to advanced deployment features for a robust and scalable distributed platform to internal and external users. This paper explains the best practices and advanced approaches to design and manage the metadata for a distributed global SAS Visual Analytics environment. Designing and building the architecture of a distributed SAS Visual Analytics environment requires attention to various important factors like user groups and roles, access management, data protection, data volume control, performance requirements and so on | sas management console, grid computing, sample, sas administrator | source | 18 |
| 118 | Designed to Fail: Approximately Right vs Precisely Wrong | Pratapsinh Patil, Aaron Raymond, Sachin Verma |
Electrolux is one of the largest appliance manufacturers in the world. Electrolux North America sells more than 2,000 products to end consumers through 9,000 business customers. To grow and increase profitability under challenging market conditions, Electrolux partnered with SAS to implement an integrated platform for Demand Driven Planning & Optimization (DDPO), and improve service levels to its customers. The process utilizes historical order data to create a statistical monthly forecast. The Electrolux team then reviews the statistical forecast in Collaborative Planning Workbench (CPW) where they can add value based on their business insights & promotional information. This improved monthly forecast is broken down to the weekly level where it flows into Inventory Optimization Workbench (IOW). IOW will then compute weekly inventory targets to satisfy the forecasted demand at the des ired service level. This presentation will also cover how Electrolux implemented this project | inventory optimization, data cleaning, excel, demand forecasting | source | 7 |
| 119 | Designing SAS Visual Analytics Reports: Write Once, View Anywhere | Karen Mobley, Rich Hogan, Pratik Phadke |
When you create reports in SAS Visual Analytics, you automatically have reports that work on mobile devices. How do you ensure that the reports are easy to use and understand on all of your desktops, tablets, and phones? This paper describes how you can design powerful reports that your users can easily view on all their devices. You'll also learn how to deliver reports to users effortlessly, ensuring that they always have the latest reports. Examples will show you tips and techniques to use that create the best possible reports for all devices. The paper provides sample reports that you can download and interactively view on your own devices. These reports include before and after examples that illustrate why the recommended best practices are important. By using these tips and techniques you'll learn how to design a report once and have confidence that it can be viewed anywhere | bar chart, sample | source | 17 |
| 120 | Detecting Phishing Attempts with SAS: Minimally Invasive Email Log Data | Taylor B. Anderson, Denise J. McManus |
Presentation | source | 5 | |
| 121 | Developing an On-Demand Web Report Platform Using Stored Processes and SAS Web Application Server | Romain Miralles | As SAS programmers, we often develop listings, graphs, and reports that need to be delivered frequently to our customers. We might decide to manually run the program every time we get a request, or we might easily schedule an automatic task to send a report at a specific date and time. Both scenarios have some disadvantages. If the report is manual, we have to find and run the program every time someone request an updated version of the output. It takes some time and it is not the most interesting part of the job. If we schedule an automatic task in Windows, we still sometimes get an email from the customers because they need the report immediately. That means that we have to find and run the program for them. This paper explains how we developed an on-demand report platform using SAS Enterprise Guide, SAS Web Application Server, and stored processes | sas programmer, sas enterprise guide, dataset, macro variable, excel | source | 18 |
| 122 | Developing SAS Studio Repositories | Swapnil Ghan, Michael Monaco, Amy Peters |
This paper covers developing SAS Studio repositories. SAS Studio introduced a new way of developing custom tasks using an XML markup specification and the Apache Velocity templating language. SAS Studio repositories build on this framework and provide a flexible way to package custom tasks and snippets. After tasks and snippets are packaged into a repository, they can be shared with users inside your organization or outside your organization. This paper uses several examples to help you create your first repository | sas studio, sample | source | 16 |
| 123 | Diagnosing Obstructive Sleep Apnea: Using Predictive Analytics Based on Wavelet Analysis in SAS/IML Software and Spectral Analysis in PROC SPECTRA | Woranat Wongdhamma | This paper presents an application based on predictive analytics and feature-extraction techniques to develop the alternative method for diagnosis of obstructive sleep apnea (OSA). Our method reduces the time and cost associated with the gold standard or polysomnography (PSG), which is operated manually, by automatically determining the OSA's severity of a patient via classification models using the time-series from a one -lead electrocardiogram (ECG). The data is from Dr. Thomas Penzel of Philipps -University, Germany, and can be downl oaded at www.physionet.org. The selected data consists of 10 recordings (7 OSAs, and 3 controls) of ECG collected overnight, and non -overlapping-minute-by-minute OSA episode annotations (apnea and non-apnea states). This accounts for a total of 4,998 events (2,532 non-apnea and 2,466 apnea minutes) | proc freq, proc step, proc iml, validation set, proc hpsplit, proc hpforest, data cleaning, sample, train-test split, data mining | source | 12 |
| 124 | Differentiate Yourself | Kirk Paul Lafler | Today's employment and business marketplace is highly competitive. As a result, it is necessary for SAS professionals to differentiate themselves from the competition. Success depends on a number of factors, including positioning yourself with the necessary technical skills in relation to the competition. This presentation illustrates how SAS professionals can acquire a wealth of knowledge and enhance their skills by accessing valuable and free web content related to SAS. With the aid of a web browser and the Internet, anyone can access published PDF papers, Microsoft Word documents, Microsoft PowerPoint presentations, comprehensive student notes, instructor lesson plans, hands-on exercises, webinars, audios, videos, a comprehensive technical support website maintained by SAS, and more to acquire the essential expertise that is needed to cut through all the marketplace noise and begin differentiating yourself to secure desirable opportunities with employers and clients | sas programmer, sas consultant | source | 19 |
| 125 | Disparities in the Receipt of Cardiac Revascularization Procedures | C. Perry Brown, Jontae Sanders |
Cardiovascular (CVD) disease is the number one cause of mortality in the United States. The mortality rate from CVD has decreased over the past 40 years due to a combination of lifestyle changes (smoking cessation, blood pressure and cholesterol screening, exercise) as well as advances in CVD therapies disparities in mortality rates continue to exist. This research sought to build on a body of research by examining the receipt of three invasive cardiac revascularization procedures, cardiac catheterization (CC), percutaneous transluminal cardiac angioplasty (PTCA) and coronary artery bypass graft (CABG) among Hispanics and Non-Hispanic Whites in Florida from 1992 to 2010 Who were diagnosed with Ischemic Heart Disease. Inpatient discharge data from the Florida Agency for Health Care Administration was utilized for this study. These data exist in SPSS format and were imported into SAS for data management and analysis | proc step, logistic regression, proc freq, multivariate analysis, logistic distribution, regression analysis | source | 13 |
| 126 | Do It Yourself (DIY) Data: Creating a Searchable Data Set of Available Classrooms using SAS Enterprise BI Server | Nicole Jagusztyn | At a community college, there was a need for college employees to quickly and easily find available classroom time slots for the purposes of course scheduling. The existing method was time-consuming and inefficient, and there were no available IT resources to implement a solution. The Office of Institutional Research, which had already been delivering reports using SAS Enterprise BI Server, created a report called "Find an Open Room" to fill the need. By combining SAS programming techniques, a scheduled SAS Enterprise Guide project, and a SAS Web Report Studio report delivered within the SAS Information Delivery Portal, a report was created that allowed college users to search for available time slots | proc sort, sas enterprise guide, proc step | source | 6 |
| 127 | Do SAS High-Performance Statistics Procedures Really Perform Highly ? A Comparison of HP and Legacy Procedures | Diep T. Nguyen, Sean Joo, Anh P. Kellermann, Jeffrey D. Kromrey, Jessica Montgomery, Patricia Rodríguez de Gil, Yan Wang |
The new SAS high -performance (HP) statistics procedures were developed to respond to the growth of big data and computing capabilities. Although there is some documentation regarding how to use these new procedures, relatively little ha s been disseminated regarding under what specific conditions users can expect performance improvements. This paper serves as a practical guide to getting started with HP procedures in SAS. The paper describe s the differences between key HP procedures (HPGENSELECT, HPLOGISTIC, HPNLMOD, HPREG, HPCORR, and HPSUMMARY) and their legacy counterparts both in terms of capability and performance, with a particular focus on discrepancies in Real Time required to execute. Simulation s were conducted to generate data sets that var ied on number of observations (10,000, 50,000, 100,000, 500,000, 1,000,000, and 10,000,000) and number of variables (50, 100, 500, 1000) to create these comparisons | cpu time, sample size, sample, normal distribution, proc means, poisson distribution, predictor variable, logistic distribution, proc genmod, proc step, proc logistic | source presentation |
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| 128 | Document and Enhance Your SAS Code, Data Sets, and Catalogs with SAS Functions, Macros, and SAS Metadata | Roberta J. Glass, Louise S. Hadden |
Discover how to document your SAS programs, data sets, and catalogs with a few lines of code that include SAS functions, macro code, and SAS metadata. Do you start every project with the best of intentions to document all of your work, and then fall short of that aspiration when deadlines loom? Learn how your programs can automatically update your processing log. If you have ever wondered who ran a program that overwrote your data, SAS has the answer! And If you don't want to be tracing back through a year's worth of code to produce a codebook for your client at the end of a contract, SAS has the answer! | proc step, proc contents, macro variable, excel, proc datasets, dataset, proc freq | source zip |
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| 129 | Dynamic Dashboards Using Base-SAS Software | Kirk Paul Lafler | Dynamic interactive visual displays known as dashboards are most effective when they show essential graphs, tables, statistics, and other information where data is the star. The first rule for creating an effective dashboard is to keep it simple. Striking a balance between content and style, a dashboard should be void of excessive clutter so as not to distract and obscure the information displayed. The second rule of effective dashboard design involves displaying data that meets one or more business or organizational objectives. To accomplish this, the elements in a dashboard should convey a format easily understood by its intended audience. Attendees learn how to create dynamic interactive user- and data-driven dashboards, graphical and table- driven dashboards, statistical dashboards, and drill-down dashboards with a purpose | proc step, bar chart, vertical bar chart, proc format, if-then-else, dataset, ods html, proc template, footnote statement, proc print, proc sgpanel, user-defined format, histogram | source | 16 |
| 130 | Dynamic Forecast Aggregation | Michael J. Leonard | Many organizations need to report forecasts of large numbers of time series at various levels of aggregation. Numerous model-based forecasts that are statistically generated at the lowest level of aggregation need to be combined to form an aggregate forecast that is not required to follow a fixed hierarchy. The forecasts need to be dynamically aggregated according to any subset of the time series, such as from a query. This paper proposes a technique for large-scale automatic forecast aggregation and uses SAS Forecast Server and SAS/ETS software to demonstrate this technique |
time series analysis, confidence interval, proc calis, series plot, time series data | source | 9 |
| 131 | Easier Platform Administration using SAS 9.4 Grid Options Sets | Andrew Howell | For any SAS Platform Administrator, the challenge is to ensure the right jobs run for the right users with the right resources at the right time. Running on a SAS/GRID greatly assists this task, allowing prioritisation of processes to ensure an optimal mix of batch processing & interactive sessions, ensuring critical tasks have the resources to run, as well as ensuring one user or group does not dominate the resources which should be available to all. In versions of SAS prior to v9.4, different user group requirements and/or different SAS application requirements necessitated multiple SAS Application Server Contexts (typically one per Business Unit), and often required complex nesting of SAS configuration files, each to suit particular resource requirements of a particular Logical SAS Server for a particular user group |
sas grid, sas management console, sas enterprise guide, data mining | source | 10 |
| 132 | Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide | Marje Fecht | Whether you have been programming in SAS for years, are new to it, or have dabbled with SAS Enterprise Guide before, this hands-on workshop sheds some light on the depth, breadth, and power of the Enterprise Guide environment. With all the demands on your time, you need powerful tools that are easy to learn and deliver end-to-end support for your data exploration, reporting, and analytics needs. Included are the following: data exploration tools formatting code-cleaning up after your coworkers enhanced programming environment (and how to calm it down) easily creating reports and graphics producing the output formats you need (XLS, PDF, RTF, HTML) workspace layout start-up processing notes to help your coworkers use your processes. This workshop uses SAS Enterprise Guide 7.1, but most of the content is applicable to earlier versions | sas enterprise guide, bar chart, scatter plot, vertical bar chart, proc freq, proc step | source | 17 |
| 133 | Effective ways of handling various file types and importing techniques using SAS9.4 | Divya Dadi, Rahul Jhaver |
Presentation | source | 4 | |
| 134 | Efficiently Create Rates over Different Time Periods (PROC MEANS and PROC EXPAND) | Thomas Gant | This session illustrates how to quickly create rates over a specified period of time using the MEANS and EXPAND procedures. If you want to know how to use the power of SAS to create a year-to-date or rolling 12-month rate across several dimensions, this paper is for you. At Kaiser Permanente, we use this technique to develop Emergency Department (ED) use rates, ED admit rates, patient day rates, readmission rates, and more. Each of these metrics is important to understanding how the membership of a health care organization utilizes its hospital services | proc step, variable list, by group processing, class variable | source | 9 |
| 135 | Empowering People to Use SAS as a Weapon for Work Reduction | Anita Measey | You have SAS Enterprise Guide installed. Y ou use SAS Enterprise Guide in your day to day work . You see how SAS Enterprise Guide can be an aid to accessing data and insightful analytics. Y ou have people you work with or support that are new to SAS and want to learn. You have people you work with or support that don't particularly want to code but will use the G UI and wizard interfaces within SAS Enterprise Guide …….. and then you have the spreadsheet addict , the one person or group of people who refuse to even sign onto SAS. This group needs to consume the data sitting in SAS, they need to do analysis but want to do it all in a spreadsheet . But, you need to retain an audit trail of the data, you have to reduce the operational risk of using spreadsheets for reporting . What do you do? How do you deal with this? This paper hopes to share some of the challen ges and triumphs in empowering these , very different groups of people, using SAS | sas enterprise guide, job security, excel | source | 9 |
| 136 | Enhanced Segmentation Using SAS Visual Analytics and SAS Visual Statistics | Darius S. Baer, Suneel Grover |
What will your customer do next? Customers behave differently; they are not all average. Segmenting your customers into different groups enables you to provide relevant communications and interactions, resulting in greater customer satisfaction as well as increased profits. SAS Visual Analytics and SAS Visual Statistics enable you to visually analyze your marketable universe to create more useful segments for customer relationship management and to understand the value and relative importance of different customer attributes. You can segment your customers by using the following methods: 1) Business rules 2) Quantile membership 3) Supervised classification 4) Unsupervised clustering Whichever way you choose, SAS enables you to graphically represent your customer dat a with respect to demographic, geographic, and customer behavioral dimensions | proc hpsplit, data validation rules, heatmap, regression analysis, credit risk, data governance, cluster analysis, customer segmentation, proc freq, logistic regression, data science, logistic distribution | source | 19 |
| 137 | Ensemble Modeling: Recent Advances and Applications | Wendy Czika, Ye Liu |
Ensemble models are a popular class of methods for combining the posterior probabilities of two or more predictive models to create a potentially more accurate model. This paper summarizes the theoretical background of recent ensemble techniques and presents examples of real-world applications. Examples of these novel ensemble techniques include weighted combinations (such as stacking or blending) of predicted probabilities in addition to averaging or voting approaches that combine the posterior probabilities by adding one model at a time. Fit statistics across several data sets are compared to highlight the advantages and disadvantages of each method, and process flow diagrams that can be used as ensemble templates for SAS Enterprise Miner are presented | scatter plot, posterior probability, regression analysis, data mining, proc hpsplit, data cleaning, neural network, proc reg, proc varclus, feature selection, validation set, macro definition, elastic net, response variable | source | 20 |
| 138 | Ensuring that Your SAS Infrastructure Is Able to Meet Your SAS Users' Demands | Margaret A. Crevar | Now that you have deployed SAS, what should you do to ensure it continues to meet your SAS users' performance expectations? This paper discusses how to proactively monitor your SAS infrastructure, with tools that should be used on a regular basis to keep tabs on infrastructure performance and housekeeping. Basic SAS administration concepts are discussed, with references for blogs, communities, and the new visual learning sites | dataset, sas grid, sas log, sas enterprise guide, data mining | source | 11 |
| 139 | Enterprise Data Governance across SAS and Beyond | Jeff Stander | As Data Management professionals, you have to comply with new regulations and controls. One such regulation is BCBS 239 in Financial Services. To respond to these new demands, you have to put processes and methods in place to automate metadata collection and analysis, and to provide rigorous documentation around your data flows. You also have to deal with many aspects of data management including data access, data manipulation (ETL and other), data quality, data usage, and data consumption often from a variety of toolsets that are not necessarily from a single vendor | data validation rules, data cleaning, data lineage, data governance | source | 20 |
| 140 | Equivalence Tests | Fei Wang, John Amrhein |
Motivated by the frequent need for equivalence tests in clinical trials, this paper provides insights into tests for equivalence. We summarize and compare equivalence tests for different study designs, including designs for one-sample problem, designs for two-sample problem (paired observations, and two unrelated samples), and designs with multiple treatment arms. Power and sample size estimation are discussed. We also give examples to implement the methods using the FREQ, TTEST, MIXED, and POWER procedures in SAS/SAT software |
sample, equivalence testing, null hypothesis, sample size, alternative hypothesis, proc step, binomial distribution, confidence interval, proc power, proc ttest, normal distribution, proc freq, standard deviation | source | 9 |
| 141 | Evaluation of a Customer's Life Cycle Time-to-Offer Cross-sales in a Bank, Based on the Behavior Score Using Logit Models & DTMC in SAS | Oscar Javier Cortés Arrigui, Miguel Angel Diaz Rodriguez |
Scotiabank Colombian division - Colpatria, is the national leader in terms of providing credit CARDS, with more than 1,600,000 active CARDS-the equivalent to a portfolio of 700 million dollars approximately. The behavior score is used to offer credit CARDS through a cross-sell process, which happens only if customers have completed six months on books after using their first product with the bank. This is the minimum period of time requested by the behavior model. The six months on books internal policy sug gests that the maturation of the client in this period is adequate, but this has never been proven. The following research aims to evaluate this hypothesis and calculate the appropriate time to offer cross -sales to new customers using Logistic Regression (Logit), while also segmenting these sales targets by their level of seniority using Discrete-Time Markov Chains (DTMC) |
regression analysis, transition matrix, credit card, markov chain, logistic regression, logistic distribution, standard deviation, proc reg | source | 13 |
| 142 | Evaluation of PROC IRT Procedure for Item Response Modeling | Yi-Fang Wu | Presentation | source | 4 | |
| 143 | Event Stream Processing for Enterprise Data Exploitation | Tho H. Nguyen, Fiona McNeill |
With the big data throughputs generated by event streams, organizations can opportunistically respond with low-latency effectiveness. Having the ability to permeate identified patterns of interest throughout the enterprise requires deep integration between event stream processing and foundational enterprise data management applications. This paper describes the innovative ability to consolidate real -time data ingestion with controlled and disciplined universal data access - from SAS and Teradata | stream processing, real-time processing, big data, data science | source | 7 |
| 144 | Everyone CAN be a Data Scientist: Using SAS Studio to Create a Custom Task for SAS In-Memory Statistics | Stephen Ludlow | SAS In-Memory Statistics uses a powerful interactive programming interface for analytics, aimed squarely at the data scientist. We show how the custom tasks that you can create in SAS Studio (a web- based programming interface) can make everyone a data scientist! We explain the Common Task Model of SAS Studio, and we build a simple task that carries out the basic functionality of the IMSTAT procedure. This task can then be shared amongst all users, empowering everyone on their journey to becoming a data scientist. During the presentation, it will become clear that not only can shareable tasks be created but the developer does not have to understand coding in Java, JavaScript, or ActionScript. We also use the task we created in the Visual Programming perspective in SAS Studio |
sas studio, data science, where statement, response variable, proc genmod, histogram, correlation, price elasticity, proc freq, regression analysis | source | 20 |
| 145 | Evolving Fraud Types: Can Your Application Keep Up? | Gordon Robinson | Does the rapidly changing fraud and compliance landscape make it difficult to adapt your applications to meet your current needs? Fraudsters are constantly evolving and your applications need to be able to keep up. No two businesses are ever the same. They all have different business drivers, customer needs, market demands, and strategic objectives. Using SAS Visual Investigator, business units can quickly adapt to their ever-changing needs and deliver what end users and customers need to be effective investigators. Using the administrative tools provided, users can quickly and easily adapt the pages and data structures that underpin applications to fit their needs | entity resolution, insurance claims, policy, proc freq, outlier, claims fraud | source | 11 |
| 146 | Exact Logistic Models for Nested Binary Data in SAS | Kyle Irimata, Jeffrey Wilson |
The use of logistic models for independent binary data has relied first on asymptotic theory and later on exact distributions for small samples, as discussed by Troxler, Lalonde, and Wilson (2011). While the use of logistic models for dependent analysis based on exact analyses is not common, it is usually presented in the case of one-stage clustering. We present a SAS macro that allows the testing of hypotheses using exact methods in the case of one-stage and two-stage clustering for small samples. The accuracy of the method and the results are compared to results obtained using an R program | logistic distribution, logistic regression, sample, regression analysis, hypothesis testing, correlation, macro call, likelihood function | source | 6 |
| 147 | Exploring SAS Embedded Process Technologies on Hadoop | David Ghazaleh | SAS Embedded Process offers a flexible, efficient way to leverage increasing amounts of data by injecting the processing power of SAS where ever the data lives. SAS Embedded Process can tap into the massively parallel processing (MPP) architecture of Hadoop for scalable performance. Using SAS in- database technologies for Hadoop, you can run scoring models generated by SAS Enterprise Miner or, with SAS In-Database Code Accelerator for Hadoop, user-written DS2 programs in parallel. With SAS Embedded Process on Hadoop you can also perform data quality operations, and extract and transform data using SAS Data Loader. This paper explores key SAS technologies that run inside the Hadoop parallel processing framework and prepares you to get started with SAS In -Database Code Accelerator and Scoring Accelerator for Hadoop | sas base, proc step, libname statement, parallel processing, data mining, by group processing, hive, proc print, set statement, big data, macro variable, user-defined format, proc contents, score code | source | 17 |
| 148 | Exploring the Factors That Impact Injury Severity Using Hierarchical Linear Modeling (HLM) | Pradeep Bista | binary variable, proc glimmix, class variable, proc step, data mining, predictor variable, proc mixed, response variable | source | 9 | |
| 149 | exSPLINE That: Explaining Geographic Variation in Insurance Pricing | Carol Frigo, Kelsey Osterloo |
Generalized linear models (GLMs) are commonly used to model rating factors in insurance pricing. The integration of territory rating and geospatial variables poses a unique challenge to the traditional GLM approach. Generalized additive models (GAMs) offer a flexible alternative based on GLM principles, with a relaxation of the linearity assumption. We will explore two approaches for incorporating geospatial data in a pricing model using a GAM based framework. The ability to incorporate new geospatial data and improve traditional approaches to territory ratemaking results in further market segmentation and a better match of price to risk. Our discussion highlights the use of the high-performance GAMPL procedure, which is new in SAS/STAT 14.1 software. With PROC GAMPL, we can incorporate the geographic effects of geospatial variables on target loss outcomes. We will illustrate two approaches |
degrees of freedom, proc genmod, pure premium, proc step, proc gam, regression analysis, response variable | source | 6 |
| 150 | Extending the Armed Conflict Location & Event Data Project with SAS Contextual Analysis | Tom Sabo | The Armed Conflict Location & Event Data Project (ACLED) is a comprehensive public collection of conflict data for developing states. As such, the data is instrumental in informing humanitarian and development work in crisis and conflict-affected regions. The ACLED project currently manually codes for eight types of events from a summarized event description, which is a time -consuming process. In addition, when determining the root causes of the conflict across developing states, these fixed event types are limiting. How can researchers get to a deeper level of analysis? This paper showcases a repeatable combination of exploratory and classification -based text analytics provided by SAS Contextual Analysis, applied to the publicly available ACLED for African states | proc hpsplit, dataset, proc sql, sas enterprise guide | source | 14 |
| 151 | Extracting Useful Information From the Google Ngram Data Set: A General Method to Take the Growth of the Scientific Literature into Account | Aurélien Nicosia, Thierry Duchesne, Samuel Perreault |
regression analysis, data cleaning | source | 14 | |
| 152 | Factors of Multiple Fatalities in Car Crashes | Bill Bentley, Gina Colaianni, Cheryl Joneckis, Sherry Ni, Kennedy Onzere |
roc curve, stepwise selection, logistic regression, odds ratio, sample, logistic distribution, anova, proc freq, regression analysis, nominal variable, data cleaning, one-way anova, response variable, proc varclus, training set, proc step, correlation, histogram, time series analysis | source | 60 | |
| 153 | FASHION, STYLE "GOTTA HAVE IT" COMPUTE DEFINE BLOCK | Christopher J. Boniface | Do you create complex reports using PROC REPORT? Are you confused by the COMPUTE BLOCK feature of PROC REPORT? Are you even aware of it? Maybe you already produce reports using PROC REPORT, but suddenly your boss needs you to modify some of the values in one or more of the columns. Maybe your boss needs to see the values of some rows in boldface and others highlighted in a stylish yellow. Perhaps one of the columns in the report needs to display a variety of fashionable formats (some with varying decimal places and some without any decimals). Maybe the customer needs to see a footnote in specific cells of the report. Well, if this sounds familiar then come take a look at the CALL DEFINE statement within a COMPUTE BLOCK of PROC REPORT. This paper will show a few tips and tricks of using the CALL DEFINE statement with conditional IF/THEN logic to make your reports stylish and fashionable |
proc report, proc step, excel, proc format, index, sas base | source | 6 |
| 154 | File Management Using Pipes and X Commands in SAS | Emily Sisson | SAS for Windows can be an extremely powerful piece of software, not only for analyzing data, but also for organizing and maintaining output and permanent datasets. By employing pipes and operating system ('X') commands within a SAS session, you can easily and effectively manage files of all types stored on your local network | dataset, call execute, data step | source zip |
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| 155 | Finding and Evaluating Multiple Candidate Models for Logistic Regression | Bruce S. Lund | Logistic regression models are commonly used in direct marketing and consumer finance applications . In this context the paper discusses two topics about the fitting and evaluation of logistic regression models. Topic #1 is a comparison of two methods for finding multiple candidate models. The first method is the familiar "best subsets" approach. Then best subsets is compared to a proposed new method based on combining models produced by backward and forward selection plus the predictors considered by backward and forward. This second method uses HPLOGISTIC with selection of models by SBC (Schwarz Bayes). Topic #2 is a discussion of model evaluation statistics to measure predictive accuracy and goodness-of-fit in support of the choice of a final model. Base SAS and SAS/STAT are used |
degrees of freedom, logistic distribution, logistic regression, proc logistic, proc step, validation set, regression analysis, data cleaning, likelihood function, class variable, sample, forward selection, best subsets, binary variable, feature selection, proc hplogistic, sample size | source zip |
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| 156 | Finding National Best Bid and Best Offer - Quote by Quote | Mark Keintz | U.S. stock exchanges (currently there are 12) are tracked in real time via the Consolidated Trade System (CTS) and the Consolidated Quote System (CQS). CQS contains every updated quote from each of these exchanges, covering some 8,500 stock tickers. It provides the basis by which brokers can honor their fiduciary obligation to investors to execute transactions at the best price, i.e. at the NBBO (National Best Bid or Best Offer). With the advent of electronic exchanges and high frequency trading (timestamps are published to the microsecond), data set size (approaching 1 billion quotes requiring 80 gigabytes of storage for a normal trading day) has become a major operational consideration for market behavior researchers recreating NBBO values. This presentation demonstrates a straightforward use of hash tables for tracking constantly changing quotes for each ticker/exchange combination to provide the NBBO for each ticker at each time point in the trading day | hash table, where statement | source | 6 |
| 157 | Finding the One: Using SAS Code and SAS Data Quality Server to Create a Matched Record | Elena Shtern, Kim Hare |
In this era of data analytics, you are often faced with a challenge of joinin g data from multiple legacy systems. When the data systems share a consistent merge key, such as ID or SSN, the solution is straightforward. However, what do you do when there is no common merge key? If one data system has a character value ID field, another has an alphanumeric field, and the only common fields are the names or addresses or dates of birth, a standard merge query does not work. This paper demonstrates fuzzy matching methods that can overcome this obstacle and build your master record through Base SAS coding. The paper also describes how to leverage the SAS Data Quality Server in SAS code | data cleaning, conditional do loop, sas base, demographics table, macro loop, do loop, proc step, autocall macro, proc sql, create table, sample, data standardization, proc sort | source | 25 |
| 158 | Fitting Multilevel Hierarchical Mixed Models Using PROC NLMIXED | Raghavendra Kurada | Hierarchical nonlinear mixed models are complex models that occur naturally in many fields. The NLMIXED procedure's ability to fit linear and nonlinear models with standard or general distributions enables you to fit a wide range of such models. SAS/STAT 13.2 enhanced PROC NLMIXED to support multiple RANDOM statements, enabling you to fit nested multilevel mixed models. This paper uses an example to illustrate the new functionality |
proc mixed, proc step, estimate, pk parameter, population pk, poisson distribution, proc glimmix, response variable | source | 15 |
| 159 | Fitting Your Favorite Mixed Models with PROC MCMC | Gordon Brown, Fang K. Chen, Maura E. Stokes |
The popular MIXED, GLIMMIX, and NLMIXED procedures in SAS/STAT software fit linear, generalized linear, and nonlinear mixed models, respectively. These procedures take the classical approach of maximizing the likelihood function to estimate model parameters. The flexible MCMC procedure in SAS/STAT can fit these same models by taking a Bayesian approach. Instead of maximizing the likelihood function, PROC MCMC draws samples (using a variety of sampling algorithms) to approximate the posterior distributions of model parameters. Similar to the mixed modeling procedures, PROC MCMC provides estimation, inference, and prediction. This paper describes how to use the MCMC procedure to fit Bayesian mixed models and compares the Bayesian approach to how the classical models would be fit with the familiar mixed modeling procedures. Several examples illustrate the approach in practice |
proc step, proc mixed, proc glimmix, covariance, random intercept, posterior probability, prior probability, nominal variable, response variable, regression analysis, likelihood function, sample, main effect, poisson distribution, covariance structure, binomial distribution | source | 27 |
| 160 | Forecasting Behavior with Age-Period-Cohort Models How APC Predicted the US | Mortgage Crisis | Presentation | proc step, correlation, proc genmod | source | 39 |
| 161 | Forecasting Frequency of Terrorist attacks in United States and Predicting the Factors that determine Success of Attacks using SAS Forecast Studio and SAS Enterprise Miner | Sri Harsha Ramineni, Koteswarrao Sriram |
Presentation | source | 5 | |
| 162 | Forecasting Short Time Series Using SAS Forecast Server | Cobey Abramowski, Christian Haxholdt, Chris Houck, Benjamin Perryman |
SAS Forecast Server provides an excellent tool towards forecasting of time series across a variety of scenarios and industries. Given sufficient data, SAS Forecast Server can provide insights into seasonality, trend, and effects of input variables and events. In some business cases, there might not be sufficient data within individual series to produce these complex models. This paper presents an approach to forecasting these short time series. Using sufficiently long time series as a training data set, forecasted shape and volumes are created through clustering and attribute-based regressions, allowing for new series to have forecasts based on their characteristics. These forecasts are passed into SAS Forecast Server through preprocessing. Production of artificial history and input variables, matched with a custom model addition to the standard model repository, allows for future values of the artificial variable to be realized as the forecast | time series analysis, proc step, incremental load, training set, neural network, proc hpforest, regression analysis | source | 5 |
| 163 | Four Lines of Code: Using Merge and Colons to Construct Historical Data from Status Tables | Jingyu She | Collection of customer data is often done in status tables or snapshots, where, for example, for each month, the values for a handful of variables are recorded in a new status table whose name is marked with the value of the month. In this QuickTip, we present how to construct a table of last occurrence times for customers using a DATA STEP merge of such status tables and the colon (":") wildcard. If the status tables are sorted, this can be accomplished in four lines of code (where RUN; is the fourth). Also, we look at how to construct delta tables (for example, from one time period to another, or which customers have arrived or left) using a similar method of merge and colons |
work library, merge statement | source | 2 |
| 164 | From SAS Data Management to Big Data Appliances: How SAS/ACCESS Makes Life Easier | Magali Thesias | In the fast changing world of Data Management, new technologies to handle increasing volumes of (big) data are emerging every day. Many (large) companies are struggling in dealing with these technologies and more importantly on integrating them in their existing data management processes. Moreover, they also want to rely on the knowledge built by their teams in existing products and implementing change to learn new technologies can therefore be a costly procedure. SAS is perfectly fitting in this situation by offering a suite of software that can be set up to work with any third-party database through the usage of the corresponding SAS/ACCESS . Indeed, for every new database technology SAS is releasing a specific SAS/ACCESS allowing users to develop and migrate SAS solutions almost transparently. Only few techniques have to be known by your users to combine the power of SAS with a third-party (big) database |
proc sql, in-database processing, sql pass-through, options statement, missing value, create table, implicit sql pass-through, libname statement, stored procedure, proc step | source presentation |
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| 165 | From Sensors to Solutions: An Example of Analytics in Motion | Milad Falahi, Gilbert Hernandez |
Oil and gas wells encounter many types of adverse events, including unexpected shut-ins, scaling, rod pump failures, breakthrough, and fluid influx, to name but a few common ones. This paper presents a real-time event detection system that presents visual reports and alerts petroleum engineers when an event is detected in a well. This system uses advanced time series analysis on both high- and low- frequency surface and downhole measurements. This system gives the petroleum engineer the ability to switch from passive surveillance of events, which then require remediation, to an active surveillance solution, which enables the engineer to optimize well intervention strategies. Events can occur simultaneously or rapidly, or can be masked over longer periods of time. We tested the proposed method on data received from both simulated and publicly available data to demonstrate how a multitude of well events can be detected by using multiple models deployed into the data stream | time series analysis, data mining, proc hpsplit, correlation, seasonal decomposition, sas model manager, missing value, proc freq | source | 12 |
| 166 | Fuzzy Matching: Where Is It Appropriate and How Is It Done? SAS Can Help. | Stephen B. Sloan, Daniel Hoicowitz |
When attempting to match names and addresses from different files, we often run into a situation where the names are similar, but not exactly the same. Sometimes there are additional words in the names, sometimes there are different spellings, and sometimes the businesses have the same name but are located thousands of miles apart. The files that contain the names might have numeric keys that cannot be matched. Therefore, we need to use a process called "fuzzy matching" to match the names from different files. The SAS function COMPGED, combined with SAS character-handling functions, provides a straightforward method of applying business rules and testing for similarity | data validation rules, character constant | source | 10 |
| 167 | Gathering Customer Information from a Mobile Application | Jim Adams | customer experience, usage data | source | 7 | |
| 168 | Generalized Linear Models for Non-Normal Data | Theresa H. Ngo | Analysts find the standard linear regression and analysis-of-variance models to be extremely convenient and useful tools. The standard linear model equation form is observations = (sum of explanatory variables) + residual with the assumptions of normality and homogeneity of variance. However, these tools are unsuitable for non-normal response variables in general. Using various transformations can stabilize the variance. However, transforming the estimates back to their original scale and interpreting the results of the analysis can be complicated. Also these transformations are often ineffective because they fail to address the skewness problem. In such cases, we reach the limits of the standard linear model. Generalized linear models have greater power to identify model effects as statistically significant when the data are not normally distributed (Stroup xvii) | response variable, estimate, proc reg, regression analysis, proc calis, correlation, likelihood function | source | 5 |
| 169 | Generating Color Scales in SAS: 256 Shades of RGB | Jeff Grant, Mahmoud Mamlouk |
Color is an important aspect of data visualization and provides an analyst with another tool for identifying data trends. But is the default option the best for every case? Default color scales may be familiar to us; however, they can have inherent flaws that skew our perception of the data. The impact of data visualizations can be improved with a little thought toward choosing the correct colors. A simple technique is presented, detailing how you can use SAS to generate a number of color scales and apply them to your data. Other techniques can be used to apply pre-defined color maps on your data in order to emphasize singularities or volumes. Using just a few graphics procedures, you can transform almost any complex data into an easily digestible set of visuals. The techniques used in this discussion were developed and tested using SAS Enterprise Guide 5.1 under SAS 9.3 | proc step, scatter plot, data visualization, proc sql, proc template | source |
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| 170 | Get a Quick Start with SAS ODS Graphics By Teaching Yourself | Roger D. Muller | SAS Output Delivery System (ODS) Graphics started appearing in SAS 9.2. When first starting to use these tools, the traditional SAS/GRAPH software user might come upon some very significant challenges in learning the new way to do things. This is further complicated by the lack of simple demonstrations of capabilities. Most graphs in training materials and publications are rather complicated graphs that, while useful, are not good teaching examples. This paper contains many examples of very simple ways to get very simple things accomplished. Over 20 different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The use of the SGPLOT, SGPANEL, and SGSCATTER procedures are shown. In addition, the paper addresses those situations in which the user must alternatively use a combination of the TEMPLATE and SGRENDER procedures to accomplish the task at hand |
proc step, proc template, sas enterprise guide, proc sgplot, proc sgpanel, sample, sas programmer | source presentation |
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| 171 | Get Out of DATA Step Code and into Quality Knowledge Bases | Brian Rineer | SAS programmers spend hours developing DATA STEP code to clean up their data. Save time and money and enhance your data quality results by leveraging the power of the SAS Quality Knowledge Base (QKB). Knowledge Engineers at SAS have developed advanced contex t-specific data quality operations and packaged them in callable objects in the QKB. In this session a Senior Software Development Manager at SAS explains the QKB and shows how to invoke QKB operations in a DATA STEP and in various SAS Data Management offerings |
data cleaning, data governance, proc sql | source | 14 |
| 172 | Getting Data into Your SAS Cloud Environment | Gary Mehler | As more of your work gets performed in an off-premise cloud environment, understanding how to get the data you want to analyze and report on becomes important. In addition, working in a Cloud environment like Amazon Web Service may be something that is new to you or your organization when using abilities in a package like SAS Visual Analytics. This presentation discusses how to get the best use out of Cloud resources, how to efficiently transport information between systems, as well as how to continue to leverage on-premise DBMS data in your future Cloud system | dataset, in-memory analytics | source | 7 |
| 173 | Getting More from the Singular Value Decomposition (SVD): Enhance Your Models with Document, Sentence, and Term Representations | Russell Albright, James A. Cox, Ning Jin |
Since its inception, SAS Text Miner has used the singular value decomposition (SVD) to convert a term - document matrix to a representation that is crucial for building successful supervised and unsupervised models. In this presentation, using SAS code and SAS Text Miner, we compare these models with those that are based on SVD representations of subcomponents of documents. These more granular SVD representations are also used to provide further insights into the collection. Examples featuring visualizations, discovery of term collocations, and near-duplicate subdocument detection are shown | reference data, proc step, proc mds, training set | source | 15 |
| 174 | Getting There from Here: Lifting Enterprise SAS to the Amazon Public Cloud | Ethan Merrill, Bryan Harkola |
If your organization already deploys one or more software solutions via Amazon Web Services (AWS), you know the value of the public cloud. AWS provides a scalable public cloud with a global footprint, allowing users access to enterprise software solutions anywhere at any time. Although SAS began long before AWS was even imagined, many loyal organizations driven by SAS are moving their local SAS analytics into the public AWS cloud, alongside other software hosted by AWS. SAS Solutions OnDemand has assisted organizations in this transition. In this paper, we describe how we extended our enterprise hosting business to AWS. We describe the open source automation framework from which SAS Solutions OnDemand built our automation stack, which simplified the process of migrating a SAS implementation. We'll provide the technical details of our automation and network footprint, a discussion of the technologies we chose along the way, and a list of lessons learned | api, ftp | source | 7 |
| 175 | Graph a Million with the SGPLOT Procedure | Prashant Hebbar, Lingxiao Li, Sanjay N. Matange |
In today's world of torrential data, plotting large amounts of data has its own challenges. The SGPLOT procedure in SAS Output Delivery System (ODS) Graphics offers a simple yet powerful tool for your graphing needs. In this paper we present some graphs that work better for large data sets. We create heat maps, box plots, parallel coordinate plots that visualize large data. You can make your graphs resilient to your growing data with ODS Graphics! |
heatmap, scatter plot, box plot, proc sgplot, series plot, sample, proc freq | source zip |
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| 176 | Graphical Results in Clinical Oncology Studies | Nora Ruel | This paper will discuss various approaches to presenting oncologic data, and focus on ways each of the graphics can be customized and individualized based on the needs of a study. The topics will range from using SGPLOT and SGPANEL procedures - simple but visually effective ways to present stratified information - to using a complex integration of a series of HIGHLOW plots which depict duration of patient responses on a clinical trial. It will also introduce other graphs that are commonly used in oncology, and discuss how they can be tailored to fit the needs of each study and presentation |
proc step, waterfall chart, proc sgplot, bar chart, best response, proc template, proc freq, clinical trial | source | 14 |
| 177 | Graphing Made Easy for Project Management | Zhouming Sun | Project management is a hot topic across many industries, and there are multiple commercial software packages for managing tasks available. The reality however is that the majority of project management software is not applicable for daily usage. SAS has a solution for this issue that can be used for managing tasks graphically in real-time. This paper introduces a new paradigm for project management utilizing the SAS Graph Template Language (GTL). SAS clients can use GTL to visualize in real-time resourcing, task assignments, delivery tracking, and project status across multiple task levels for more efficient project management | graph template language, sample | source | 11 |
| 178 | Greenspace: A Macro to Improve a SAS Data Set Footprint | Brian K. Varney | SAS programs can be very I/O intensive. SAS data sets with inappropriate variable attributes can degrade the performance of SAS programs. Using SAS compression offers some relief but does not eliminate the issue of inappropriately defined SAS variables. This paper intends to examine the problems inappropriate SAS variable attributes can cause as well as a macro to tackle the problem of minimizing the footprint of a SAS Data Set | character variable, dataset, proc step, proc sql, cpu time, proc contents, macro variable, view, length statement | source | 7 |
| 179 | Hands-On GTL | Kriss Harris | Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? W ould you like to know how to easily create life sciences industry standard graphs such as adverse event timelines, Kaplan-Meier plots, and waterfall plots? Finally, would you like to learn all these methods in a relaxed environment that fosters questions? Great-this topic is for you! In this hands-on workshop, you will be guided through the Graph Template Language (GTL). You will also complete fun and challenging SAS graphics exercises to enable you to more easily retain what you have learned. This session is structured so that you will learn how to create the standard plots that your manager requests, how to easily create simple ad hoc plots for your customers, and also how to create complex graphics | box plot, class variable, graph template language, histogram, life sciences | source | 19 |
| 180 | Have it Your Way: Creating Reports with the DATA Step Report Writing Interface | Peter J. Lund | SAS provides some powerful, flexible tools for creating reports, like PROC REPORT and PROC TABULATE. With the advent of the Output Delivery System (ODS) you have almost total control over how the output from those procedures looks. But, there are still times where you need (or want) just a little more and that's where the Report Writing Interface (RWI) can help. The Report Writing Interface is just a fancy way of saying you're using the ODSOUT object in a DATA STEP. This object allows you to layout the page, create tables, embed images, add titles and footnotes and more - all from within a DATA STEP, using whatever DATA STEP logic you need. Also, all the style capabilities of ODS are available to you so that your DATA STEP created output can have fonts, sizes, colors, backgrounds and borders to make your report look just like you want |
data step, ods pdf, proc report, proc step, calendar year, output delivery system, proc template, set statement, create table, conditional do loop | source | 27 |
| 181 | Have SAS Annotate Your Blank CRF for You! Plus Dynamically Add Color and Style to Your Annotations | Steven C. Black | Annotating a blank Case Report Form (blankcrf.pdf, which is required for a nondisclosure agreement with the US Food and Drug Administration) by hand is an arduous process that can take many hours of precious time. Once again SAS comes to the rescue! You can have SAS use the Study Data Tabulation Model (SDTM) specifications data to create all the necessary annotations and place them on the appropriate pages of the blankcrf.pdf. In addition you can dynamically color and adjust the font size of these annotations. This approach uses SAS, Adobe Acrobat's forms definition format (FDF) language, and Adobe Reader to complete the process. In this paper I go through each of the steps needed and explain in detail exactly how to accomplish each task | crf, proc sql, cdisc | source zip |
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| 182 | Health Care's One Percenters : Hot-Spotting to Identify Areas of Need and Opportunity | Paul A. LaBrec | Since Atul Gawande popularized the term in describing the work of Dr. Jeffrey Brenner in a New Yorker article, "hot-spotting" has been used in health care to describe the process of identifying "super -utilizers" of health care services, then defining intervention programs to coordinate and improve their care. According to Brenner's data from Camden, New Jersey, 1% of patients generate 30% of payments to hospitals, while 5% of patients generate 50% of payments. Analyzing administrative health care claims data, which contains information about diagnoses, treatments, costs, char ges, and patient sociodemographic data, can be a useful way to identify super-users, as well as those who may be receiving inappropriate care. Both groups can be targeted for care management interventions. In this paper, techniques for patient outlier identification and prioritization are discussed using examples from the CMS linkable 2008-2010 Medicare Data Entrepreneurs' Synthetic Public Use File (DE-SynPUF) | insurance claims, sample, cohort analysis, proc step, dataset, regression analysis, proc sql | source | 14 |
| 183 | Helpful Hints for SAS 9.4 | Lisa Cook, Edith Jeffries, Cindy Taylor |
A group tasked with testing SAS software from the customer perspective has gathered a number of helpful hints for SAS 9.4 that will smooth the transition to its new features and products. These hints will help with the "Huh?" moments that crop up when you are getting oriented and will provide short, straightforward answers. We also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS Customer Experience Testing shares insiders' practical tips to ensure that you are ready to begin your transition to SAS 9.4 and guidance for after you are at 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, and administering the SAS 9.4 environment. This paper was first published in 2012; it has been revised each year since with new information | sas studio, sas model manager, customer experience | source | 18 |
| 184 | High-Performance Data Access with FedSQL and DS2 | Shaun Kaufmann | SAS Federated Query Language (FedSQL) is a SAS proprietary implementation of the ANSI SQL:1999 core standard capable of providing a scalable, threaded, high-performance way to access relational data in multiple data sources. This paper explores the FedSQL language in a three-step approach. First, we introduce the key features of the language and discuss the value each feature provides. Next, we present the FEDSQL procedure (the Base SAS implementation of the language) and compare it to PROC SQL in terms of both syntax and performance. Finally, we examine how FedSQL queries can be embedded in DS2 programs to merge the power of these two high-performance languages |
proc sql, proc step, set statement, sas base, relational database, create table, oracle, sql pass-through | source | 13 |
| 185 | Highly Customized Graphs Using ODS Graphics | Warren F. Kuhfeld | Y ou can use annotation, modify templates, and change dynamic variables to customize graphs in SAS. Standard graph customization methods include template modification (which most people use to modify graphs that analytical procedures produce) and SG annotation (which most people use to modify graphs that procedures such as PROC SGPLOT produce). However, you can also use SG annotation to modify graphs that analytical procedures produce. Y ou begin by using an analytical procedure, ODS Graphics, and the ODS OUTPUT statement to capture the data that go into the graph. Y ou use the ODS document to capture the values that the procedure sets for the dynamic variables, which control many of the details of how the graph is created. Y ou can modify the values of the dynamic variables, and you can modify graph and style templates |
sg annotation, proc step, if-then-else, macro definition | source zip |
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| 186 | Holiday Demand Forecasting in the Utility Industry | Rain Xie, Alex Chien |
Electric load forecasting is a complex problem that is linked with social activity considerations and variations in weather and cli mate. Furthermore, electricity is one of only a few goods that has almost no inventory . The absence of inventory means that electric utilities' operations depend heavily on accurate load forecasts to balance the demand and supply in an almost real-time fashion. This electric load forecasting problem is even more challenging for holidays, which have limited historical data and varying demand patterns. These challenges result in the forecast error for holidays being higher, on average, than it is for regular days. This paper investigates three practical holiday electric demand forecasting techniques: modeling holidays as weekends, modeling holidays using holiday dummy variables, and a two-stage method in which the second stage models residuals | class variable, training set, gradient boosting, binary variable, demand modeling, proc glm, proc genmod, sample, regression analysis | source | 13 |
| 187 | How Do My Neighbors Affect Me? SAS/ETS Methods for Spatial Econometric Modeling | Guohui Wu, Jan Chvosta |
Contemporary data-collection processes usually involve recording information about the geographic location of each observation. This geospatial information provides modelers with opportunities to examine how the interaction of observations affects the outcome of interest. For example, it is likely that car sales from one auto dealership might depend on sales from a nearby dealership either because the two dealerships compete for the same customers or because of some form of unobserved heterogeneity common to both dealerships. Knowledge of the size and magnitude of the positive or negative "spillover" effect is important for creating pricing or promotional policies. This paper describes how geospatial methods are implemented in SAS/ETS and illustrates some ways you can incorporate spatial data into your modeling toolkit |
response variable, poisson distribution, regression analysis, binomial distribution, proc step, proc freq | source | 13 |
| 188 | How Managers and Executives Can Leverage SAS Enterprise Guide | Steven J. First | SAS Enterprise Guide is an extremely valuable tool for programmers, but it should also be leveraged by managers and executives to do data exploration, get information on the fly, and take advantage of the powerful analytics and reporting that SAS has to offer. This can all be done without learning to program. This paper gives an overview of how SAS Enterprise Guide can improve the process of turning real-time data into real-time business decisions by managers | sas enterprise guide, sas programmer, sas enterprise guide project | source | 6 |
| 189 | How to Create Data-Driven Lists | Kate Burnett-Isaacs | As SAS programmers we often want our code or program logic to be driven by the data at hand, rather than be directed by us. Such dynamic code enables the data to drive the logic or sequence of execution. This type of programming can be very useful when creating lists of observations, variables, or data sets from ever-changing data. Whether these lists are used to verify the data at hand or are to be used in later steps of the program, dynamic code can write our lists once and ensure that the values change in tandem with our data. This Quick Tip paper will present the concepts of creating data-driven lists for observations, variables, and data sets, the code needed to execute these tasks, and examples to better explain the process and results of the programs we will create | macro variable, macro loop, do loop, select, dictionary tables, macro definition, put statement, proc sql, macro put, proc step, where statement, work library | source | 8 |
| 190 | How to Create Event Stream Processing Models via a Graphical User Interface | Lei Xiao, Fang Meng |
SAS Event Stream Processing is designed to quickly analyze and process large volumes of streaming data in motion. The new version of SAS Event Stream Processing introduces a browser-based user interface that enables you to create and test event stream processing models in the visual drag-and-drop environment. This environment delivers a highly interactive and intuitive user experience. This paper describes the visual interactive interface for building models to monitor event stream activity. It provides examples that demonstrate how you can easily build a model using SAS Event Stream Processing Studio, the graphical user interface of SAS Event Stream Processing. In these examples, SAS Event Stream Processing serves as the front end to process high-velocity streams. On the back end, SAS Real-Time Decision Manager consumes events and makes the final decision to push the suited offer to the customer. This paper explains the concepts of windows, retention policies, edges, and connectors | stream processing, real-time processing, web service | source | 14 |
| 191 | How to Find Your Perfect Match Using SAS Data Management | Mary Kathryn Queen | SAS Data Management is not a dating application. However, as a data analyst, you do strive to find the best matches for your data. Similar to a dating application, when trying to find matches for your data, you need to specify the criteria that constitutes a suitable match. You want to strike a balance between being too stringent with your criteria and under-matching your data and being too loose with your criteria and over-matching your data. This paper highlights various SAS Data Management matching techniques that you can use to strike the right balance and help your data find its perfect match. As a result, you can improve your data for reporting and analytics purposes | data analyst, entity resolution | source | 10 |
| 192 | How to Leverage the Hadoop Distributed File System as a Storage Format for the SAS Scalable Performance Data Server and SAS Scalable Performance Data Engine | Steven Sober | The SAS Scalable Performance Data Server and SAS Scalable Performance Data Engine are data formats from SAS that supports the creation of analytical base tables with tens of thousands of columns. These analytical base tables are used to support daily predictive analytical routi nes. Traditionally Storage Area Network (SAN) storage has been and continues to be the primary storage platform for the SAS Scalable Performance Data Server and SAS Scalable Performance Data Engine formats. Due to cost constrains associated with SAN storage, companies have added Hadoop to their environments to help minimize storage costs. In this paper we explore how the SAS Scalable Performance Data Server and SAS Scalable Performance Data Engine leverage the Hadoop Distributed File System (HDFS) | where statement, proc step, proc sql, cpu time, libname statement, parallel processing, sas log, create table, proc hplogistic, sas administrator | source | 19 |
| 193 | How to Maintain Happy SAS 9 Users | Margaret A. Crevar | Today's SAS environment has large numbers of concurrent SAS processes that have to process ever-growing data volumes. To help SAS users remain productive, SAS administrators must ensure that SAS applications have sufficient computer resources, properly configured and monitored often. Understanding how all of the components of SAS work and how they are used by your users is the first step. The guidance offered in this paper helps SAS administrators evaluate hardware, operating system, and infrastructure options for a SAS environment that will keep their SAS applications running at optimal performance and keep their user community happy | business intelligence, sas grid, sas batch, batch job, dataset, proc step, relational database, proc sort | source | 11 |
| 194 | How to Make Your SAS Web Applications More Secure: Top Ten Tips | Heesun Park | Most SAS products are offered as web-based applications these days. Even though web-based interface provides unmatched accessibility, it comes with known security vulnerabilities. This paper examines the most common exploitation scenarios and suggests the ways to make web applications more secure. The top ten focus areas include protection of user credentials, use of stronger authentication methods, implementation of SSL for all communications between client and server, understanding of attacking mechanism, penetration testing, adoption and integration with third-party security packages, encryption of any sensitive data, security logging and auditing, mobile device access management, and prevention of threats from inside. INTRODUCTION: OVERVIEW OF SAS 9.4 WEB APPLICATION CONFIGURATION SAS 9.4 web-based applications are packaged with common SAS 9.4 mid-tier platform applications that include SAS Web Server and SAS Web Application Server | suspicious activity, pii | source | 9 |
| 195 | How to Move Data among Client Hard Disk, the Hadoop File System, and SAS LASR Analytic Server | Yue Qi | In SAS LASR Analytics Server, the data can reside in three types of environments: client hard disk (e.g. laptop), Hadoop File System (HDFS) and memory of the LASR Analytic Server. How to move the data efficiently among these is critical to get the insights behind da ta on time. In this paper, we will illustrate all the possible ways to mov e the data including 1) moving data from client hard disk to HDFS; 2) moving data from HDFS to client hard disk; 3) moving data from HDFS to LASR Analytic Server; 4) moving data from LASR Analytic Server to HDFS; 5) moving data from client hard disk to LAS R Analytic Server; 6) moving data from LASR Analytic Server to client hard disk. I | proc step, libname statement | source | 4 |
| 196 | How to Speed Up Your Validation Process Without Really Trying | Alice M. Cheng, Justina M. Flavin, Michael Wise |
The COMPARE procedure in SAS offers relatively simple, yet powerful way to perform validation. In this paper, the authors will not only provide a brief introduction of the COMPARE procedure, but will also share their techniques to speed up the validation process. These techniques include the standard use of options and the clever use of features in PROC COMPARE. Also introduced are the use of &SYSINFO which summarizes the result of the comparison by a single numeric number, the macros %QCDATA and %QCDIR to validate a single dataset and all the datasets in two directories, respectively. KEYWORDS: PROC COMPARE, Validation, Without Automation, &SYSINFO, With Automation |
proc compare, proc step, data cleaning, out=, sysrc, call execute, by group processing, where statement, clinical trial, adam dataset, cdisc | source | 20 |
| 197 | How to STRIP Your Data: Five Go-To Steps to Assure Data Quality | Michael Santema, Fagen Xie |
Managing large datasets comes with the task of providing a certain level of quality assurance, no matter what the data is used for. We present here the fundamental SAS procedures to perform when determining the completeness of a dataset. Even though each dataset is unique and has its own variables that need more examination in detail, it is important to first examine the size, time, range, interactions, and purity (STRIP) of a dataset to determine its readiness for any use. This paper covers first steps you should always take, regardless of whether you're dealing with health, financial, demographic, environmental, or other data | proc step, proc freq, quality assurance, time variable, proc sql, character variable, numeric variable, big data, missing value | source | 6 |
| 198 | How to Use PERL Functions in SAS: Some Basic Examples | Peter Timusk | Presentation | source | 7 | |
| 199 | How to Visualize SAS Data with JavaScript Libraries like HighCharts and D3 | Vasilij Nevlev | Presentation | source zip |
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| 200 | If You Build It, Will They Understand? Designing Reports for the General Public in SAS Visual Analytics | Jesse Sookne, Julianna Langston, Karen Mobley, Ed Summers |
Your organization already uses SAS Visual Analytics and you have designed reports for internal use. Now you want to publish a report on your external website. How do you design a report for the general public considering the wide range of education and abilities? This paper defines best practices for designing reports that are universally accessible to the broadest audience. You learn tips and techniques for designing reports that the general public can easily understand and use to gain insight. You also learn how to leverage features that help you comply with your legal obligations regarding people with disabilities. The paper includes recommendations and examples that you can apply to your own reports | bar chart, line chart, time series analysis, scatter plot, series plot | source | 20 |
| 201 | Implementing a Credit Risk Management Dashboard with SAS | Boaz Galinson | In my presentation I describe how to facilitate better credit risk decision-making throughout the organization by implementing a credit risk dashboard with SAS and what are the common pitfalls to avoid. Better credit decision making means that banks need to improve their data risk aggregation in order to effectively identify and manage their credit exposures and credit risk, create early warning signs, and improve the ability of risk managers to challenge the business and independently assess and address evolving changes in credit risk. My presentation focuses on using SAS Credit Risk Dashboard to achieve all of the above. Clearly, you can use my method and principles of building a credit risk dashboard to build other dashboards for other types of risks as well (market, operational, liquidity, compliance, reputation, etc.). In addition, because every bank must integrate the various risks with a holistic view, each of the risk dashboards can be the foundation for building an effective enterprise risk management (ERM) dashboard that takes into account correlation of risks, risk tolerance, risk appetite, breaches of limits, capital allocation, risk-adjusted return on capital (RAROC), and so on. This will support the actions of top management so that the bank can meet shareholder expectations in the long term | credit risk, credit risk analyst | source | 6 |
| 202 | Implementing Hashing Techniques in SAS | Richard D. Langston | Although hashing methods such as SHA256 and MD5 are already available in SAS , users may need other methods. This presentation shows how hashing and methods can be implemented using SAS DATA steps and macros, with judicious use of the bitwise functions (BAND, BOR, and so on) and by referencing public domain sources | comment statement, macro definition | source | 13 |
| 203 | Importing Data Directly from PDF into SAS Data Sets | William Wu, Steven Li, Yun Guan |
Creating electronic documents in PDF file format is becoming increasingly popular nowadays. Combining ODS PDF statement and the REPORT procedure in SAS can create various PDF output files with different styles. During the process of validating those data in PDF file, there is demand to import PDF summary tables or listings into SAS datasets. A utility is developed which reads in a SAS generated uncompressed PDF file, extracts and converts the data from the PDF file into a SAS datasets. The overview of this utility is presented in this paper |
dataset, ods pdf | source | 13 |
| 204 | Importing Metadata Programmatically Using the SAS Batch Import Tool | David Moors | Importing metadata can be a time-consuming, painstaking, and fraught task when you have multiple SAS packages to deal with. Fortunately SAS software provides a batch facility to import (and export) metadata by using the command line, thereby saving you from many a mouse click and potential repetitive strain injury when you use the Metadata Import Wizard! This paper aims to demystify the SAS batch import tool. It also introduces a template-driven process for programmatically building a batch file that contains the syntax needed to import metadata and then automatically execute the batch import scripts. THE ISSUE Having been a SAS developer, Tester & Administrator during my SAS career one of the most time consuming and potentially frustrating tasks has to be importing metadata packages between SAS environments | macro variable, excel, file statement, dataset, do loop, sas log, macro loop, macro definition, spk file, macro parameter, proc import, put statement, proc step | source zip |
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| 205 | Improve Your Business through Process Mining | Emily Y. Gao, Robert Chu, Xudong Sun |
Looking for new ways to improve your business? Try mining your own data! Event log data is a side product of information systems generated for audit and security purposes and is seldom analyzed, especially in combination with business data. Along with the cloud computing era, mor e event log data has been accumulated and analysts are searching for innovative ways to take advantage of all data resources in order to get valuable insights. Process mining, a new field for discovering business patterns from event log data, has recently proved useful for business applications. Process mining shares some algorithms with data mining but it is more focused on interpretation of the detected patterns rather than prediction. Analysis of these patterns can lead to improvements in the efficiency of common existing and planned business processes. Through process mining, analysts can uncover hidden relationships between resources and activities and make changes to improve organizational structure | create table, proc sql, proc freq, data mining, proc step, sample | source | 17 |
| 206 | Improving Health Care Quality with the RAREEVENTS Procedure | Bucky Ransdell | Statistical quality improvement is based on understanding process variation, which falls into two categories: variation that is natural and inherent to a process, and unusual variation due to specific causes that can be addressed. If you can distinguish between natural and unusual variation, you can take action to fix a broken process and avoid disrupting a stable process. A control chart is a tool that enables you to distinguish between the two types of variation. In many health care activities, carefully designed processes are in place to reduce variation and limit adverse events. The types of traditional control charts that are designed to monitor defect counts are not applicable to monitoring these rare events, because these charts tend to be overly sensitive, signaling unusual variation each time an event occurs. In contrast, specialized rare events charts are well suited to monitoring low-probability events | exponential distribution, histogram | source | 11 |
| 207 | Improving Performance of Memory-Based Reasoning Model by Using Weight of Evidence Coded Categorical Variables | Vinoth Kumar Raja, Goutam Chakraborty, Vignesh Dhanabal |
Memory based Reasoning (MBR) is an empirical classification method which works by comparing cases in hand with similar examples from the past and then applying that information to the new case. MBR modeling is based on the assumptions that the input variables are numeric, orthogonal to each other, and standardized. The latter two assumptions are taken care by Principal Components' transformation of raw variables and using the components instead of the raw variables as inputs to MBR. To satisfy the first assumption, the categorical variables are often dummy coded. This raises issues such as increasing dimensionality and overfitting in the training data by introducing discontinuity in the response surface relating inputs and target variables. The Weight of Evidence (WOE) method overcomes this challenge. This method measures the relative response of the target for each group level of a categorical variable | nominal variable, response variable, data mining, interval variable, numeric variable, training set | source | 6 |
| 208 | Increasing Efficiency by Parallel Processing | Shuhua Liang | Data volume is increasing exponentially across industries and working with big data is often time consuming and challenging. The primary goal in programming is to maximize throughputs while minimize the use of execution time and resources. By utilizing the Multiprocessing (MP) Connect method on a Symmetric Multiprocessing (SMP) computer, a programmer can divide a job into independent tasks and execute threads in parallel on multiple processors. This paper demonstrates the development and application of a parallel processing program on large sets of healthcare data in SAS | parallel processing, proc sort, proc step, elapsed time | source presentation |
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| 209 | Infographics Powered by SAS Visual Analytics and SAS Office Analytics | Travis Murphy | A picture is worth a thousand words, but what if there are a billion words? This is where the picture becomes even more important, and this is where infographics step in. Infographics are a representation of information in a graphic format designed to make the data easily understandable, at a glance, without having to have a deep knowledge of the data. Because of the amount of data available today, more infographics are being created to communicate the information and insight from all available data, both in the boardroom and on social media. This session shows you how to create information graphics that can be printed, shared, and dynamically explored with objects and data from SAS Visual Analytics. Connect your infographics to the high-performance analytical engine from SAS for repeatability, scale, and performance on big data and for ease of use | data visualization, big data, excel, business intelligence, analytics platform, sas enterprise guide | source | 15 |
| 210 | Innovative Performance Improvements through Automated Flowcharts in SAS | Steven J. First, Jennifer First-Kluge |
One of the tedious but necessary things that SAS programmers must do is to trace and monitor SAS runs: counting observations and columns, checking performance, drawing flow charts, and diagramming data flows. This is required to be able to audit results, find errors, find long running steps, identify large data files, and to simply understand what a particular job does. Enterprise Guide includes functionality to help with some of this, on some SAS jobs. This paper presents an innovative custom tool that automatically produces flow charts -- showing all SAS steps in a job, file counts in and out, variable counts, recommendations and generated code for performance improvements, and more. The system was written completely in SAS using SAS source programs, SAS logs, PROC SCAPROC output, and more as input. This tool can eliminate much of the manual work needed in job optimization, space issues, debugging, change management, documentation, and job checkout |
sas log, proc step, cpu time, record count, dataset, sas enterprise guide, proc sql, macro variable | source | 14 |
| 211 | Instant Interactive SAS Log Window Analyzer | Amarnath Vijayarangan, Palanisamy Mohan |
An interactive SAS environment is preferred for developing programs as it gives the flexibility of instantly viewing programming log in the Log window. The programmer must review the log window to ensure that each and every single line of a written program is running successfully without displaying any messages defined by SAS that are potential errors. The number of lines generated in the log window varies depending on the complexity of the program. Reviewing the log window every time is not only time consuming but also prone to manual error for any level of programmer. Even to confirm that the log is free from error, the programmer must check the log. Currently in the interactive SAS environment there is no way to get an instant notification about the generated log from the Log window, indicating whether there have been any messages defined by SAS that are potential errors | sas log, sas enterprise guide, sas base, sas studio, macro definition | source | 7 |
| 212 | Integrated Marketing with SAS in the Age of Real Time | Dmitriy Alergant, Marje Fecht |
Real-time, integrated marketing solutions are a necessity for maintaining your competitive advantage. This paper provides a brief overview of three SAS solutions (SAS Marketing Automation, SAS Real-Time Decision Manager, and SAS Event Stream Processing) that form a basis for building a modern, real-time, interactive marketing platform. It presents typical (and also possible) use cases that you can implement with a comprehensive real-time interactive marketing solution, in major industries like finance (banking), telco, and retail. It demonstrates typical functional architectures that need to be implemented to support business cases (how solution components collaborate with customer's IT landscape and with each other). * And it provides examples of our experience in implementing these solutions-do's and don'ts, best practices, and what to expect from an implementation project | stream processing, real-time processing, data mart, web service | source | 14 |
| 213 | Integrating Microsoft VBScript and SAS | Christopher Johnson | VBScript and SAS are each powerful tools in their own right. These two technologies can be combined so that SAS code can call a VBScript program or vice versa. This gives a programmer the ability to automate SAS tasks, traverse the file system, send emails programmatically, manipulate Microsoft Word, EXCEL, and PowerPoint files, get web data, and more. This paper will present example code to demonstrate each of these capabilities. Contents |
excel | source zip |
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| 214 | Integrating SAS and R to Perform Optimal Propensity Score Matching | Lucy D'Agostino McGowan, Robert Greevy |
In studies where randomization is not possible, imbalance in baseline covariates (confounding by indication) is a fundamental concern. Propensity score matching (PSM) is a popular method to minimize this potential bias, matching individuals who received treatment to those who did not to reduce the imbalance in pre-treatment covariate distributions. PSM methods continue to advance as computing resources expand. Optimal matching, which selects the set of matches that minimizes the average difference in propensity scores between mates, has been shown to outperform less computationally intensive methods. However, many find the implementation daunting. SAS/IML software allows the integration of optimal matching routines that execute in R, e.g. the R nbpMatching package. This paper walks through performing optimal PSM in SAS through implementing R functions |
propensity score, propensity score matching, histogram, proc step, baseline characteristics, proc iml, sample | source | 11 |
| 215 | Intermediate ODS Graphics | Chuck Kincaid | This paper will build on the knowledge gained in the Intro to SAS ODS Graphics. The capabilities in ODS Graphics grow with every release as both n ew paradigms and smaller tweaks are introduced. After talking with the ODS developers, a selection of the many wonderful capabilities was selected. This paper will look at that selection of both types of capabilities and provide the reader with more tools for their belt. Visualization of data is an important part of telling the story seen in the data. And while the standards and defaults in ODS Graphics are very well done, sometimes the user has specific nuances for characters in the story or additional plot lines they want to incorporate. Almost any possibility, from drama to comedy to mystery, is available in ODS Graphics if you know how. We will explore tables, annotation and changing attributes, as well as the BLOCK plot. Any user of Base SAS on any pl atform will find great value from the SAS ODS Graphics procedures |
proc step, proc sgplot, bar chart, scatter plot, output delivery system, nominal variable, line chart | source | 28 |
| 216 | Introducing SAS Grid Manager for Hadoop | Cheryl Doninger, Doug Haigh |
Organizations view Hadoop as a lower cost means to assemble their data in one location. Increasingly, the Hadoop environment also provides the processing horsepower to analyze the patterns contained in this data to create business value. SAS Grid Manager for Hadoop allows you to co-locate your SAS Analytics workload directly on your Hadoop cluster via an integration with the YARN and Oozie components of the Hadoop ecosystem. YARN allows Hadoop to become the "operating system" for your data; it is a tool that manages and mediates access to the shared pool of data, and manages and mediates the resources that are used to manipulate the pool. Learn the capabilities and benefits of SAS Grid Manager for Hadoop as well as some configuration tips. In addition, sample SAS Grid jobs are provided to illustrate different ways to access and analyze your Hadoop data with your SAS Grid jobs | sas grid, data mining | source | 10 |
| 217 | Introduction to ODS Graphics | Chuck Kincaid | This presentation teaches the audience how to use ODS Graphics. Now part of Base SAS, ODS Graphics are a great way to easily create clear graphics t hat enable any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures is explained, as well as some of the many options availa ble. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value in the SAS ODS Graphics procedures |
proc step, series plot, sas base, numeric variable, proc sgplot, proc sgpanel | source | 18 |
| 218 | Introduction to SAS ODS ExcelXP Tagset: Exporting Formulas and Tables | Veronica Renauldo | SAS ODS ExcelXP Tagset offers users the ability to export a SAS dataset, with all of the accompanying functions and calculations, to an EXCEL worksheet. For industries, this is particularly useful as not everyone is a SAS programmer but would like to have access to and manipulate data from SAS. The ExcelXP tagset is one of several built in templates for exporting data in SAS. The tagset gives programmers the ability to export functions and calculations into the cells of EXCEL. There are several options within the tagset which enable the programmer to customize the EXCEL file. Some examples of options allow the programmer to name the worksheet, style each table, embed titles and footnotes, and export multiple data tables to the same EXCEL worksheet |
excel, proc step, proc report, proc print, format statement, xls, file statement, dataset | source zip |
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| 219 | Is It Time for a PhD in Data Science? | Jennifer Priestley | Presentation | data science, machine learning | source | 15 |
| 220 | It's raining data! Harnessing the Cloud with Amazon Redshift and SAS/ACCESS Software | Chris DeHart, Jeffrey Bailey |
Every day, companies all over the world are moving their data into the Cloud. While there are many options available, much of this data will wind up in Amazon Redshift. As a SAS user, you are probably wondering, "What is the best way to access this data using SAS?" This paper discusses the many ways that you can use SAS/ACCESS to get to Amazon Redshift. We compare and contrast the various approaches and help you decide which is best for you. Topics that are discussed are building a connection, moving data into Amazon Redshift, and implementing performance best practices |
amazon redshift, amazon s3, proc sql, proc step, dataset, libname statement, sas base, proc export, sas log, benchmarking | source | 9 |
| 221 | Keeping Hope Afloat: How to Prevent Financial Loss amongst a Sea of Online Pirates | Sam Atassi, Jamie Hutton |
Gone are the days when the only method of receiving a loan was by visiting your local branch and working with a loan officer. In today's economy, financial institutions are increasingly relying on online channels to interact with their customers. The anonymity that is inherent in this channel makes it a prime target for fraudsters. The solution is to profile the behavior of Internet banking in real time and risk-assess each transaction as it is processed in order to prevent financial loss before it occurs. SAS Visual Scenario Designer enables you to create rules, scenarios, and models, test their impact, and inject them into real-time transaction processing using SAS Event Stream Processing | stream processing, real-time processing, proc hpsplit | source | 15 |
| 222 | Kerberos Delegation with SAS 9.4 | Stuart J. Rogers | Do you want to see and experience how to configure SAS Enterprise Miner single sign-on? Are you looking to explore setting up Integrated Windows Authentication with SAS Visual Analytics? This hands- on workshop demonstrates how you can configure Kerberos delegation with SAS 9.4. You see how to validate the prerequisites, make the configuration changes, and use the applications. By the end of this paper, which is also being presented as a workshop, you will be empowered to start your own configuration | sas management console, data mining | source | 19 |
| 223 | Key Features in ODS Graphics for Efficient Clinical Graphing | Yuxin Jiang | High-quality effective graphs not only enhance understanding of the data but also facilitate regulators in the review and approval process. In recent SAS releases, SAS has made significant progress toward more efficient graphing in ODS Statistical Graphics (SG) procedures and Graph Template Language (GTL). A variety of graphs can be quickly produced using convenient built-in options in SG procedures. With graphical examples and comparison between SG procedures and traditional SAS/GRAPH procedure in reporting clinical trial data, this paper highlights several key features in ODS Graphics to efficiently produce sophisticated statistical graphs with more flexible and dynamic control of graphical presentation including 1) Better control of axes in different scales and intervals; 2) Flexible ways to control graph appearance; 3) Plots overlay in single-cell or multi-cell graphs; 4) Enhanced annotation; 5) Classification panel of multiple plots with individualized labeling |
graph template language, in= option | source | 7 |
| 224 | Key Requirements For SAS Grid Users | Piyush K. Singh, Tanuj Gupta, Prasoon Sangwan |
Considering the fact that SAS Grid Manager is becoming more and more popular, it is important to fulfill the user's need for a successful migration to a SAS Grid environment. This paper focuses on key requirements and common issues for new SAS Grid users, especially if they are coming from a traditional environment. This paper describes a few common requirements like the need for a current working directory, the change of file system navigation in SAS Enterprise Guide with user-given location, getting job execution summary email, and so on. The GRIDWORK directory has been introduced in SAS Grid Manager, which is a bit different from the traditional SAS WORK location. This paper explains how you can use the GRIDWORK location in a more user-friendly way. Sometimes users experience data set size differences during grid migration. A few important reasons for data set size difference are demonstrated | sas enterprise guide, sas grid, sas management console, cpu time, data step, work library | source presentation |
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| 225 | Kicking and Screaming Your Way to SAS Enterprise Guide | Michelle Buchecker | You are a skilled SAS programmer. You can code circles around those "newbies" who point and click in SAS Enterprise Guide. And yet… there are tasks you struggle with on a regular basis, such as "Is the name of that data set DRUG or DRUGS?" and "What intern wrote this code? It's not formatted well at all and is hard to read." In this presentation you learn how to program, yes program, more efficiently. You learn the benefits of autocomplete and inline help, as well as how to easily format the code that in tern wrote that you inherited. In addition, you learn how to create a process flow of a program to identify any dead ends, i.e., data sets that get created but are not used in that program | sas enterprise guide, proc step, error message | source | 11 |
| 226 | Latent Class Analysis Using the LCA Procedure | Patricia A. Berglund | This paper presents the use of latent class analysis (LCA) to base the identification of a set of mutually exclusive latent classes of individuals on responses to a set of categorical, observed variables. The LCA procedure, a user-defined SAS procedure for conducting LCA and LCA with covariates, is demonstrated using data on substance use from Monitoring the Future, a nationally representative sample of high school seniors who are also followed at selected time points during adulthood. The demonstration includes guidance on data management prior to analysis, PROC LCA syntax requirements and options, and interpretation of output |
proc step, proc calis, estimate, degrees of freedom, odds ratio, sample, likelihood function, feature selection, confidence interval | source | 18 |
| 227 | Latent Structure Analysis Procedures in SAS | Deanna Naomi Schreiber-Gregory | The current study looks at several ways to investigate latent variables in longitudinal surveys and their use in regression models. Three different analyses for latent variable discovery will be briefly reviewed and explored. The latent analysis procedures explored in this paper are PROC LCA, PROC LTA, PROC TRAJ, and PROC CALIS. The latent variables will then be included in separate regression models. The effect of the latent variables on the fit and use of the regression model compared to a similar model using observed data will be briefly reviewed. The data used for this study was obtained via the National Longitudinal Study of Adolescent Health, a study distributed and collected by Add Health. Data was analyzed using SAS 9.4. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science and/or statistics |
latent variable, regression analysis, proc step, factor analysis, logistic distribution, logistic regression, proc calis, correlation, response variable, sample, proc means, proc surveylogistic | source | 8 |
| 228 | Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP | Mark Keintz | From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variable. For the SAS user, the central operational task is typ- ically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG func- tion, it has no analogous "lead" function - an especially significant problem in the case of large data series. This paper will (1) review the lag function, in particular the powerful, but non-intuitive implications of its queue-oriented basis, (2) demonstrate efficient ways to generate leads with the same flexibility as the lag function, but without the common and expensive recourse to data re-sorting, and (3) show how to dynamically generate leads and lags through use of the hash object | lag function, set statement, hash table, by group processing, time series analysis, missing value, hash iterator | source | 15 |
| 229 | Lenovo Success Story: Early Detection of Pervasive Quality Issues through the Voice of Customer Analytics | Murali Krishna Pagolu, Rob Carscadden |
Quality issues can easily go undetected in the field for months, even years. Why? Customers are more likely to complain through their social networks than directly through customer service channels. The Internet is the closest friend for most users and focusing purely on internal data isn't going to help. Issues surfaced through the internet have the potential to be amplified and go "viral" to the user community than issues going through traditional channels. Customer complaints are often handled by marketing or customer service and aren't shared with engineering. This lack of integrated vision is due to the silo mentality in organizations and proves costly in the long run. Information systems are designed with business rules to find "out of spec" situations or to trigger when a certain threshold of reported cases is reached before engineering is involved. Complex business problems demand sophisticated technology to deliver advanced analytical capabilities | excel, quality control, unstructured data, sample | source | 16 |
| 230 | Let SAS Help You Easily Find and Access Your Folders and Files | Ting Sa | In this paper, a SAS macro is introduced that can help users find and access their folders and files very easily. By providing a path to the macro and letting the macro know which folders and files you are looking for under this path, the macro creates an HTML report that lists the matched folders and files. The best part of this HTML report is that it also creates a hyperlink for each folder and file so that when a user clicks the hyperlink, it directly opens the folder or file. Users can also ask the macro to find certain folders or files by providing part of the folder or file name as the search criterion. T he results shown in the report can be sorted in different ways so that it can further help users quickly find and access their folders and files | macro variable, sample | source presentation |
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| 231 | Let the CAT Out of the Bag: String Concatenation in SAS 9 | Joshua M. Horstman | Are you still using TRIM, LEFT, and vertical bar operators to concatenate strings? It's time to modernize and streamline that clumsy code by using the string concatenation functions introduced in SAS 9. This paper is an overview of the CAT, CATS, CATT, and CATX functions introduced in SAS 9, and the new CATQ function added in SAS 9.2. In addition to making your code more compact and readable, this family of functions also offers some new tricks for accomplishing previously cumbersome tasks | character functions, sas 9, catq, ifc, sas programmer | source | 7 |
| 232 | Let the Schedule Manager Take Care of Scheduling Jobs in SAS Management Console 9.4 | Anjan Matlapudi | Report automation and scheduling are very hot topics in many corporate industries. Automating reports have many advantages including reducing work load, elimination of repetitive tasks, generate accurate results and better performance. In recent years SAS institute launched more powerful tools to present and share business analytics data across the world. This paper illustrates stepwise process of how to deploy and schedule reports on server using SAS Management Console 9.4. Many of us know that the scheduling jobs can be done using windows as well as server level and it is important to note that the server side scheduling has more advantages than scheduling jobs on windows. Windows scheduler invokes SAS programs on local PC and more often subject to system crashes. The main advantage scheduling on server is most jobs scheduled run during nighttime facilities faster record retrieval and less load burden on database servers | sas management console, macro variable, sas administrator, sas grid, time variable, proc sql | source | 11 |
| 233 | Leverage Your Reports in SAS Visual Analytics: Using SAS Theme Designer | Meenu Jaiswal, Ipsita Samantarai |
Is uniqueness essential for your reports? SAS Visual Analytics provides the ability to customize your reports to make them unique using SAS Theme Designer (Classic). SAS Theme Designer (Classic) can be accessed from the SAS Visual Analytics Hub to create custom themes. SAS Theme Designer (Classic) enables you to customize your reports to meet your branding needs and to ensure a unified look across your company. The report themes affect the colors, fonts, and other elements that are used in tables and graphs. The paper explores how to access SAS Theme Designer (Classic) from the SAS Visual Analytics home page, how to create and modify report themes that are used in SAS Visual Analytics, how to create report themes from imported custom themes, and how to import and export custom report themes | source | 15 | |
| 234 | Leveraging Advanced Analytics in Pricing and Inventory Decisions at a Major Durable Goods Company | Varunraj Valsaraj, Bahadir Aral, Baris Kacar, Jinxin Yi |
As a result of globalization, the durable goods market has become increasingly competitive, with market conditions that challenge profitability for manufacturers. Moreover, high material costs and the capital - intensive nature of the industry make it essential that companies understand demand signals and utilize supply chain capacity as effectively as possible. To grow and increase profitability under these challenging market conditions, a major durable goods company has partnered with SAS to streamline analysis of pricing and profitability, optimize inventory, and improve service levels to its customers. The price of a product is determined by a number of factors, such as the strategic importance of customers, supply chain costs, market conditions, and competitive prices. Offering promotions is an important part of a marketing strategy; it impacts purchasing behaviors of business customers and end consumers | source | 7 | |
| 235 | Life in the Fast Lane: Optimization for Interactive and Batch Jobs | Nikola Markovic, Gregory S. Barnes Nelson |
We spend so much time talking about GRID environments, distributed jobs, and huge da ta volumes that we ignore the thousands of relatively tiny programs scheduled to run every night, which produce only a few small data sets, but make all the difference to the users who depend on them. Individually, these jobs might place a negligible load on the system, but by their sheer number they can often account for a substantial share of overall resources, sometimes even impacting the performance of the bigger, more important jobs. SAS programs, by their varied nature, use available resources in a varied way. Depending on whether a SAS procedure is CPU-, disk- or memory-intensive, chunks of memory or CPU can sometimes remain unused for quite a while. Bigger jobs leave bigger chunks, and this is where being small and able to effectively exploit those leftovers can be a great advantage | in=, memory usage, sas batch, proc step, temporary dataset | source | 17 |
| 236 | Limit of Detection (LoD) Estimation Using Parametric Curve Fitting to (Hit) Rate Data: The LOD_EST SAS Macro | Jesse Albert Canchola, Pari Hemyari |
The Limit of Detection (LoD) is defined as the lowest concentration or amount of material, target or analyte that is consistently detectable (for PCR quantitative studies , in at least 95% of the samples tested )1. In practice, the estimation of the LoD uses a parametric curve fit to a set of panel member (PM1, PM2, PM3, etc.) data where the responses are binary. Typically, the parametric curve fit to the percent detection levels takes on the form of a probit or logistic distribution. For this, the SAS PROBIT procedure can be used to fit a variety of distributions, including both the probit and logistic. We introduce the LOD_EST SAS macro that takes advantage of the SAS PROBIT procedure's strengths and returns an information-rich graphic as well as a percent detection table with associated 95% exact (Clopper-Pearson) confidence intervals for the hit rates at each level |
macro definition, confidence interval, logistic distribution | source zip |
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| 237 | Linear State Space Models in Retail and Hospitality | Beth Cubbage | Retailers need critical information about the expected inventory pattern over the life of a product to make pricing, replenishment, and staffing decisions. Hotels rely on booking curves to set rates and staffing levels for future dates. This paper explores a linear state space approach to understanding these industry challenges, applying the SAS/ETS SSM procedure. We also use the SAS/ETS SIMILARITY procedure to provide additional insight. These advanced techniques help us quantify the relationship between the current inventory level and all previous inventory levels (in the retail case). In the hospitality example, we can evaluate how current total bookings relate to historical booking levels. Applying these procedures can produce valuable new insights about the nature of the retail inventory cycle and the hotel booking curve |
proc step, regression analysis, proc reg, estimate, proc mixed | source | 10 |
| 238 | Location, Location, Location--Analytics with SAS Visual Analytics and Esri | Murali Nori, Himesh Patel |
Business Intelligence users analyze business data in a variety of ways. Location information is a big part of business data. It is essential to combine location information, mapping and data from multiple sources like enterprise data, social media data and demographic data to provide in-depth analytical insight. New analytical capabilities are added to SAS Visual Analytics, leveraging the new partnership with ESRI, a leader in location intelligence and mapping. The new capabilities enable users to enhance the analytical insights from SAS Visual Analytics. This paper demonstrates and discusses the new partnership with ESRI and the new capabilities added to SAS Visual Analytics | business intelligence, geospatial analytics | source | 8 |
| 239 | Lyric Complexity and Song Popularity: Analysis of Lyric Composition and Relation among Billboard Top 100 Songs | John Harden, Yang Gao, Vojtech Hrdinka, Chris Linn |
Does lyric complexity impact song popularity, and can analysis of the Billboard Top 100 from 1955 -2015 be used to evaluate this hypothesis? The music industry has undergone a dramatic change. New technologies enable everyone's voice to be heard and has created new avenues for musicians to share their music. These technologies are destroying entry barriers, resulting in an exponential increase in competition in the music industry. For this reason, optimization that enables musicians and record labels to recognize opportunities to release songs with a likelihood of high popularity is critical. The purpose of this study is to determine whether the complexity of song lyrics impacts popularity and to provide guidance and useful information toward business opportunities for musicians and record labels. One such opportunity is the optimization of advertisement budgets for songs that have the greatest chance for success, at the appropriate time and for the appropriate audience | proc hpsplit, regression analysis, proc reg, response variable, data cleaning, adjusted r-squared, logistic regression, anova, sample, data mining, proc freq, logistic distribution, neural network, sas enterprise guide, likelihood function, excel | source | 15 |
| 240 | Macro Variables in SAS Enterprise Guide | Khoi To | For SAS Enterprise Guide users, sometimes macro variables and their values need to be brought over to the local workspace from the server, especially when multiple data sets or outputs need to be written to separate files in a local drive. Manually retyping the macro variables and their values in the local workspace after they have been created on the server workspace would be time-consuming and error-prone, especially when we have quite a number of macro variables and values to bring over. Instead, this task can be achieved in an efficient manner by using dictionary tables and the CALL SYMPUT routine, as illustrated in more detail below. The same approach can also be used to bring macro variables and their values from the local to the server workspace | macro variable, dictionary tables, call symput | source zip |
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| 241 | Macroeconomic Simulation Analysis for Multi-asset Class Portfolio Returns | Srikant Jayaraman, Joe Burdis, Lokesh Nagar |
Macroeconomic Simulation Analysis provides an in-depth insight to a portfolio's performance spectrum. Conventionally, portfolio and risk managers obtain macroeconomic scenarios from third parties such as the Federal Reserve and determine portfolio performance under the provided scenarios. In this paper, we propose a technique to extend scenario analysis to an unconditional simulation capturing the distribution of possible macroeconomic climates and hence, the true multivariate distribution of returns. In general, this is referred to as a top-down approach. We start with forecasting nearly 30 macroeconomic variables using Bayesian Vector Autoregression (BVAR). BVAR is used to avoid overfitting of the forecasting model. Using the model residuals, we fit copula over the marginal error distributions of each of the factors, thus taking into account the uncertainty and tail risk | proc step, macro-economic scenario, proc varmax, scenario analysis, variance, proc reg, macro variable, regression analysis, correlation | source | 6 |
| 242 | Make Predictions More Predictive: Lift Retail Marketing Campaign Response by Blending Logistic Regression and the Cox Proportional Hazards Model | Hsin-Yi Wang | Logic model produced propensity scores have been intensively employed to assist direct marketing name selections. By which, only customers with absolute higher likelihood to respond are mailed offer to in order to achieve cost reduction, thus event ROI is increased. A fly in the omelet is, compared to model building performance time window, usually 6 month to 12 month, a marketing event time period is usually much shorter. As such, this approach lacks of the ability to deselect those who with high propen sity score but unlikely to respond to an upcoming campaign. Considering to dynamically building a complete propensity model for every upcoming camping is nearly impossible, incorporating "time to respond" has been of great interests to marketer to add dimension to enhance response prediction. Hence, this paper presents an inventive modeling technique combining Logistic Regression and Cox Proportional Hazards Model | propensity score, logistic distribution, proc step, logistic regression, survival analysis, sample, proc phreg, regression analysis | source | 10 |
| 243 | Making Better Decisions about Risk Classification Using Decision Trees in SAS Visual Analytics | Stephen M. Overton, Ben Murphy |
SAS Visual Analytics Explorer puts the robust power of decision trees at your fingertips for an opportunity to visualize and explore how data is structured. Decision trees help analysts better understand discrete relationships within data by visually showing how combinations of variables lead to a target indicator. This paper explores the practical use of decision trees in SAS Visual Analytics Explorer through an example of risk classification in the financial services industry. This paper will explain various parameters and implications, explore ways the decision tree provides value, and provide alternative | proc hpsplit, response variable, type i error, predictor variable, sample, customer segmentation, aml analyst, suspicious activity | source | 13 |
| 244 | Making it Happen: A Novel Way to Save Taxpayer Dollars by Implementing an In-House SAS Data Analytics and Research Center | Vivek Sethunatesan | As part of promoting a data-driven culture and data analytics modernization at its Federal sector clientele, Northrop Grumman developed a framework for designing and implementing an in -house DAARC using an SAS set of tools. This DAARC provides a complete set of SAS Enterprise Business Intelligence (BI) and SAS Data Management tools. The platform can be used for data research, evaluations, and analysis and reviews by federal agencies such as the Social Security Administration (SSA), the Center for Medicare and Medicaid Services (CMS), and others. DAARC architecture is based on an SAS data analytics platform with newer capabilities of data mining, forecasting, visual analytics, and data integration using SAS BI. These capabilities enable developers, researchers, and analysts to explore big data sets with varied data sources, create predictive models, and perform advanced analytics including forecasting, anomaly detection, use of dashboards, and creating online reports | data science, business intelligence | source | 11 |
| 245 | Masking Data to Obscure Confidential Values: A Simple Approach | Bruce F. Gilsen | When I help users design or debug their SAS programs, they are sometimes unable to provide relevant SAS data sets because they contain confidential information. Sometimes, confidential data values are intrinsic to their problem, but often the problem could still be identified or resolved with innocuous data values that preserve some of the structure of the confidential data. Or, the confidential values are in variables that are unrelated to the problem. While techniques for masking or disguising data exist, they are often complex or proprietary. In this paper, I describe a very simple macro, REVALUE, that can change the values in a SAS data set. REVALUE preserves some of the structure of the original data by ensuring that for a given variable, observations with the same real value have the same replacement value, and if possible, observations with a different real value have a different replacement value | character variable, numeric variable, order by, macro loop, %if-%then-%else, macro variable, macro definition, dataset, proc step, proc format, length statement, sorted data, temporary array, proc sql | source | 19 |
| 246 | Mass-Scale, Automated Machine Learning and Model Deployment Using SAS Factory Miner and SAS Decision Manager | Jonathan Wexler, Steve Sparano |
Business problems have become more stratified, and micro-segmentation is driving the need for mass- scale, automated machine learning solutions. Additionally, deployment environments include diverse ecosystems, requiring hundreds of models to be built and deployed quickly via web services to operational systems. The new SAS automated modeling tool allows you to build and test hundreds of models across all of the segments in your data, testing a wide variety of machine learning techniques. The tool is completely customizable, allowing you transparent access to all modeling results. This paper shows you how to identify hundreds of champion models using SAS Factory Miner, while generating scoring web services using SAS Decision Manager. Immediate benefits include efficient model deployments, which allow you to spend more time generating insights that might reveal new opportunities, expose hidden risks, and fuel smarter, well-timed decisions | proc hpsplit, data validation rules, data science, champion model, score code, model monitoring, web service, if-then-else, model inventory, machine learning, neural network, proc hpforest, model deployment, data cleaning, imputation, regression analysis | source | 19 |
| 247 | Mastering Data Summarization with PROC SQL | Christianna S. Williams | The SQL procedure is extremely powerful when it comes to summarizing and aggregating data, but it can be a little daunting for programmers who are new to SAS or for more experienced programmers who are more familiar with using the SUMMARY or MEANS procedure for aggregating data. This hands -on workshop demonstrates how to use PROC SQL for a variety of summarization and aggregation tasks. These task s include summarizing multiple measures for groupings of interest, combining summary and detail information (via several techniques), nesting summary functions by using inline views, and generating summary statistics for derived or calculated variables. At tendees will learn how to use a variety of PROC SQL summary functions, how to effectively use WHERE and HAVING clauses in constructing queries, and how to exploit the PROC SQL remerge. The examples become progressively more complex over the course of the workshop as you gain mastery of using PROC SQL for summarizing data |
group by, proc step, proc sql, proc means, create table, macro variable, order by, sample, missing value, where statement, datepart, error message, sysdate9, class variable | source | 20 |
| 248 | MCMC in SAS: From Scratch or by PROC | Chelsea Lofland | Markov chain Monte Carlo (MCMC) algorithms are an essential tool in Bayesian statistics for sampling from various probability distributions. Many users prefer to use an existing procedure to code these algorithms, while others prefer to write an algorithm from scratch. We demonstrate the various capabilities in SAS software to satisfy both of these approaches. In particular, we first illustrate the ease of using the MCMC procedure to define a structure. Then we step through the process of using SAS/IML to write an algorithm from scratch, with examples of a Gibbs sampler and a Metropolis-Hastings random walk |
proc step, sample, random number generation, normal distribution, likelihood function, bayesian statistics, proc iml | source | 19 |
| 249 | Measuring Brand Sentiment Using Smileys | Sharat Dwibhasi | It is of paramount importance for brand managers to measure and understand consumer brand associations and the mind -space their brand captures. Brands are encoded in memory on a cognitive and emotional basis. Traditionally, brand tracking has been done by surveys and taking feedbacks, resulting in a dir ect-communication which only covers c ognitive segment and misses emotional segment. Respondents generally behave differently under observation and in solitude. In this project, a new brand tracking technique is proposed which involves capturing public data from social media that thereby focuses more on emotional aspects. For conceptualizing and testing this approach, we downloaded around one million tweets for three major brands - Nike, Adidas and Reebok posted by the users. We proposed a methodology and calculated metrics (benefits and attributes) using this data for each brand. We noticed that generally emoticons are not used in sentiment mining | data mining, proc freq | source | 8 |
| 250 | Medicare Fraud Analytics Using Cluster Analysis: How PROC FASTCLUS Can Refine the Identification of Peer Comparison Groups | Paulo Macedo | The existence and magnitude of fraud in health insurance programs, although limited to a small fraction of health care providers, requires the utilization of fraud prevention and detection procedu res. Data Mining methods are used to uncover odd billing patterns in large databases of health care claims history where efficient fraud discovery involves the preliminary step of deploying automated outlier detection techniques in order to classify identified outliers as potential fraud, before an in -depth investigation. An essential component of the outlier detection procedure is the identification of proper peer comparison groups to classify providers as within-the-norm or outliers. This study refines the concept of peer comparison group within the category of provider and considers the possibility of the existence of distinct billing patterns associated with medical or surgical procedure codes identifiable by the Berenson-Eggers Type of System (BETOS) | outlier, sample, cluster analysis, proc step, proc fastclus, proc robustreg, mahalanobis distance, sample size, data science, temporary dataset, covariance, regression analysis, proc princomp | source zip |
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| 251 | Medicare Payment Models: Past, Present, and Future | Paul Gorrell | When Medicare was first implemented in 1966 nearly half of all Americans 65 and older were without health insurance. Fifty years later only 2% lack health insurance. The difference, of course, is Medicare. Medicare now covers 55 million people, about 17% of the U.S. population, and is the si ngle largest purchaser of personal health care. But despite this success the rising costs of health care in general, and Medicare in particular, have become a growing concern. Medicare policies are important not only because of the large numbers of benefic iaries, payers, and providers directly affected, but also because they affect private-sector policies as well. Analyses of these policies and their consequences are complicated not only by the effects of an aging population with changing cost drivers (e.g. less smoking, more obesity), but also by different Medicare payment models | insurance claims, health insurance | source | 9 |
| 252 | Metadata Statistics As an Aid for Code Review | Frank Poppe, Joris Huijser |
Code review is an important tool for ensuring the quality and maintainability of an organization's ETL processes. This paper introduces a method that analyzes the metadata to assign an alert score to each job. For the jobs of each flow, the metadata is collected, giving information about the number of transformations in a job, the amount of user-written code, the number of loops, the number of empty description fields, whether naming conventions are followed, and so on. This is aggregated into complexity indicators per job. Together with other information about the jobs (e.g. the CPU-time used from logging applications) this information can be made available in SAS Visual Analytics, creating a dashboard that highlights areas for closer inspection | sas data integration studio, cpu time, sas management console, sysrc | source | 13 |
| 253 | Migrating from SAS 9.3 to SAS 9.4: SAS Communicating with Microsoft Office Products | Hong (Lily) Zhang | Microsoft Office products play an important role in most enterprises. SAS is combined with Microsoft Office to assist in decision making in everyday life. Most SAS users have moved from SAS 9.3, or 32 -bit SAS, to SAS 9.4 for its exciting new features. This paper describes a few things that do not work quite the same in SAS 9.4 as they did in the 32-bit version. It discusses the reasons for the differences and the new approaches that SAS 9.4 provides. The paper focuses on how SAS 9.4 works with EXCEL and Access. Furthermore, this presentation summarizes methods by LIBNAME engines and by import or export procedures. It also demonstrates how to interactively import or export PC files with SAS 9.4. The issue of incompatible format catalogs is addressed as well |
excel, proc step, libname statement, libname engine, proc import, catalog, proc export, dataset, proc cport, sas library | source | 10 |
| 254 | Minimizing Fraud Risk through Dynamic Entity Resolution and Network Analysis | Danielle Davis, Stephen Boyd, Ray Ong |
Every day, businesses have to remain vigilant of fraudulent activity, which threatens customers, partners, employees, and financials. Normally, networks of people or groups perpetrate deviant activity. Finding these connections is now made easier for analysts with SAS Visual Investigator (SAS VI), an upcoming SAS solution that ultimately minimizes the loss of money and preserves mutual trust among its shareholders. SAS Visual Investigator takes advantage of the capabilities of our new SAS Viya server. Investigators can efficiently investigate suspicious cases across business lines, which has traditionally been difficult. However, the time required to collect, process and identify emerging fraud and compliance issues has been costly. Making proactive analysis accessible to analysts is now more important than ever. SAS Visual Investigator was designed with this goal in mind and a key component is the visual social network view | social network analysis, entity resolution, outlier, fraud ring | source | 10 |
| 255 | Missing Values: They Are NOT Nothing | Justin Jia, Shan Shan Lin |
When analyzing data with SAS, we often encounter missing or null values in data. Missing values can arise from the availability, collectibility, or other issues with the data. They represent the imperfect nature of real data. Under most circumstances, we need to clean, filter, separate, impute, or investigate the missing values in data. These processes can take up a lot of time. For these reasons, missing values are usually unwelcome and need to be avoided in data analysis; however, there are two sides to every coin, and if we can think outside the box, we can exploit the negative features of missing values for positive uses. Sometimes, we can create and use missing values to achieve our particular goals in data manipulation and analysis. These approaches can make data analyses convenient and improve work efficiency for SAS programming. This kind of creative and critical thinking is the most valuable quality for data analysts | missing value, proc step, data science, sample, proc sql, proc tabulate, data cleaning, numeric variable, proc logistic, sas programming, frequency analysis, character variable, proc freq, proc transpose, create table, financial services, logistic distribution, proc report, balance, user-defined format, group by, proc means, clinical trial, if-then-else, validation set | source | 19 |
| 256 | Modernizing Data Management with Event Streams | Evan Guarnaccia, Fiona McNeill, Steve Sparano |
Specialized access requirements to tap into event streams vary depending on the source of the events. Open-source approaches from Spark, Kafka, Storm, and others can connect event streams to big data lakes, like Hadoop and other common data management repositories. But a different approach is needed to ensure latency and throughput are not adversely affected when processing streaming data, that is, they need to scale. This talk distinguishes the advantages of adapters and connectors and shows how SAS Event Stream Processing can leverage both Hadoop and YARN technologies to scale while still meeting the needs of streaming data analysis and large, distributed data repositories | stream processing, real-time processing, big data | source | 20 |
| 257 | Money Launderers Beware! Catching You Is Just a Point-and-Click Away | Renee Palmer, Yue Chai |
Wouldn't it be fantastic to develop and tune scenarios in SAS Visual Scenario Designer and then smoothly incorporate them into your SAS Anti-Money Laundering solution with just a few clicks of your mouse? Well, now there is a way. SAS Visual Scenario Designer is the first data-driven solution for interactive rule and scenario authoring, testing, and validation. It facilitates exploration, visualization, detection, rule writing, auditing, and parameter tuning to reduce false positives; and all of these tasks are performed using point and click. No SAS coding skills required! Using the approa ch detailed in this white paper, we demonstrate how you can seamlessly port your SAS Visual Scenario Designer scenarios into the SAS Anti-Money Laundering solution | aml, machine learning, case management, type i error, audit trail | source | 14 |
| 258 | More Hidden Base SAS Features to Impress Your Colleagues | Peter Crawford | Across the languages of SAS are many golden nuggets-functions, formats, and programming features just waiting to impress your friends and colleagues. While learning SAS for over 30 years, I have collected a few of these nuggets, and I offer a dozen more of them to you in this presentation. I presented the first dozen in a similar paper at SAS Global Forum 2015 | sas base, proc print, sas programming, macro variable, cpu time, proc step, infile statement, input statement, macro quoting function, sample, options statement, macro definition, proc sql, sas enterprise guide | source | 15 |
| 259 | Multi-Tenancy in SAS--Is It Worth the Fight? | Dileep Pournami, Christopher Blake, Ekaitz Goienola, Sergey Iglov |
At the Royal Bank of Scotland, one of our key organizational design principles is to "share everything we can share". In essence, this promotes the cross -departmental sharing of platform services. Historically, this was never enforced in our Business Intelligence platforms like SAS, resulting in a diverse technology estate, which presents challenges to our platform team for maintaining software currency, software versions, and overall quality of service. Currently, we have SAS 8.2 and SAS 9.1.3 on the mainfr ame, SAS 9.2, SAS 9.3, and SAS 9.4 across our Windows and Linux servers, and SAS 9.1.3 and SAS 9.4 on PC across the bank. One of the benefits to running a multi -tenant SAS environment is removing the need to procure, install, and configure a new envir onment when a new department wants to use SAS. However, the process of configuring a secure multi -tenant environment, using the default tools and procedures, can still be very labor intensive | business intelligence, data warehouse | source | 17 |
| 260 | Multiple-Group Multiple Imputation: An Empirical Comparison of Parameter Recovery in the Presence of Missing Data using SAS | Patricia Rodríguez de Gil, Jeffrey D. Kromrey |
It is well documented in the literature the impact of missing data: data loss (Kim & Curry, 1977) and consequently loss of power (Raaijmakers, 1999), and biased estimates (Roth, Switzer, & Switzer, 1999). If left untreated, missing data are a threat to the validity of inferences made from research results. However, the application of a missing data treatment does not prevent researchers from reaching spurious conclusions. In addition to considering the research situation at hand and the type of missing data present, another important factor that researchers should also consider when selecting and implementing a missing data treatment is the structure of the data. In the context of educational research, multiple-group structured data are not uncommon. Assessment data gathered from distinct subgroups of students according to relevant variables (e.g., socio-economic status and demographics) might not be independent | missing value, covariance, sample, proc step, effect size, imputation, multiple imputation, proc calis, covariance structure, sample size, latent variable, by group processing, missing at random, correlation, likelihood function, data science, maximum likelihood estimation | source | 15 |
| 261 | My "Excel"lent Adventure | Gina Huff | We asked for it and once again, SAS delivers! Now production in SAS 9.4 M3, ODS EXCEL provides users an extremely easy way to export output to Microsoft EXCEL. Using existing ODS techniques that you may already be familiar with (like predefined styles, traffic-lighting, and custom formatting) in tandem with common EXCEL features (like formulas, frozen headers and footers, and page setup options) you can create a publication-ready document in a snap. As a matter of fact, the new ODS EXCEL Statement is such a handy tool you may find yourself using it for more than large-scope production programs. ODS EXCEL is so straightforward that it can be handy for quick adhoc requests as well. Join me on my adventure as I explore the features of ODS EXCEL to create institutional research reports. I offer tips and techniques you can use to make your output 'EXCEL'lent, too |
excel, ods excel, output delivery system, proc step, proc tabulate, title statement, options statement, footnote statement, xls, file statement, format statement, proc report, proc freq | source | 18 |
| 262 | Name That Tune--Writing Music with SAS | Daniel R. Bretheim | Writing music with SAS is a simple process. Including a snippet of "music" in a program is a great way to signal completion of processing. It's also fun! This paper illustrates a method for translating music into SAS code using the CALL SOUND routine | proc freq, sas programmer | source | 8 |
| 263 | New for SAS 9.4: A Technique for Including Text and Graphics in Your Microsoft Excel Workbooks, Part 1 | Vincent DelGobbo | A new ODS destination for creating Microsoft EXCEL workbooks is available starting in the third maintenance release for SAS 9.4. This destination creates native Microsoft EXCEL XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation you learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented. Using the ExcelXP tagset with earlier versions of SAS to create multi-sheet workbooks is also discussed |
excel, xls, sas output, sample, proc report, proc sgplot, sas base, scatter plot, proc step, dataset, proc tabulate, format statement | source | 23 |
| 264 | New for SAS 9.4: Integrating Third-Party Metadata for Use with Lineage and Impact Analysis | Liz McIntosh | Customers are under increasing pressure to determine how data, reports, and other assets are interconnected, and to determine the implications of making changes to these objects. Of special interest is performing lineage and impact analysis on data from disparate systems (for example, SAS, IBM DataStage, Informatica PowerCenter, SAP BusinessObjects, and Cognos). SAS provides utilities to identify relationships between assets that exist within the SAS system . Beginning with the third maintenance release of SAS 9.4, these utilities support third-party products. This paper provides step- by-step instructions, tips, and techniques for using these utilities to collect metadata from these dispara te systems, and then performing lineage and impact analysis | oracle, dataset, stored procedure, data governance | source | 18 |
| 265 | Nine Frequently Asked Questions about Getting Started with SAS Visual Analytics | Patricia S. Aanderud, Ryan Kumpfmiller, Nick Welke |
You've heard all the talk about SAS Visual Analytics-but maybe you are still confused about how the product would work in your SAS environment. Many customers have the same points of confusion about what they need to do with their data, how to get data into the product, how SAS Visual Analytics would benefit them, and even should they be considering Hadoop or the cloud. In this paper, we cover the questions we are asked most often about implementation, administration, and usage of SAS Visual Analytics | business intelligence, sas enterprise guide, sas management console, data profiling, dataset, excel | source | 20 |
| 266 | No FREQ'n Way | Renee Canfield | In the consumer credit industry, privacy is key and the scrutiny increases every day. When returning files to a client, we must ensure that they are depersonalized so that the client cannot match back to any personally identifiable information (PII). This means that we must locate any values for a variable that occur on a limited number of records and null them out (i.e., replace them with missing values). When you are working with large files that have more than one million observations and thousands of variables, locating variables with few unique values is a difficult task. While the FREQ procedure and the DATA STEP merging can accomplish the task, using first./last. BY variable processing to locate the suspect values and hash objects to merge the data set back together might offer increased efficiency |
hash table, proc step, proc freq, proc sql, macro variable, macro loop, macro definition | source | 10 |
| 267 | No More Bad Dates!: A Guide to SAS Dates in Macro Language | Kate Burnett-Isaacs, Andrew W. Clapson |
The SAS macro language is an efficient and effective way to handle repetitive processing tasks. One such task is conducting the same DATA steps or procedures for different time periods, such as every month, quarter, or year. SAS dates are based on the number of days between January 1st, 1960, and the target date value, which, while very simple for a computer, is not a human-friendly representation of dates. SAS dates and macro processing are not simple concepts themselves, so mixing the two can be particularly challenging, and it can be very easy to end up working with bad dates! Un derstanding how macros and SAS dates work individually and together can greatly improve the efficiency of your coding and data processing tasks. This paper covers the basics of SAS macro processing, SAS dates, and the benefits and difficulties of using SAS dates in macros, to ensure that you never have a bad date again | macro variable, sysdate9, date variable, macro definition, macro put, put statement, &variable, macro facility, macro loop, if-then-else, macro function | source | 8 |
| 268 | Omitting Records with Invalid Default Values | Lily Yu | Many databases include default values that are set inappropriately. These default values may be scattered throughout the database. There may be no pattern to their occurrence. Records without valid information should be omitted from statistical analysis. Choosing the best method to omit records with invalid values can be difficult. Manual corrections are often prohibitively time- consuming. SAS offers useful and efficient approaches: a combined DATA STEP with SQL or SORT procedures. This paper provides a step-by-step tutorial to accomplish this task |
proc sort, proc sql, set statement, temporary dataset | source zip |
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| 269 | On The Fly SAS Reports | Ronald Palanca | In today's fast paced work environment, time management is crucial to the success of the project. Sending requests every time to SAS programmers to run reports to get the most current data can be a stretch sometimes on an already strained schedule. Why bother to contact the programmer? Why not built-in the execution of the SAS program in the report so that upon launching of the report, real time data will be retrieved and report will show most recent data. This paper will demonstrate that by opening an existing SAS report in Microsoft Word or EXCEL, the data in the report will refresh automatically. Simple Visual Basic for Applications (VBA) codes are written in MS Word or EXCEL that upon opening of an existing SAS report, the SAS programs that creates the report are called from within the MSOffice product and will overwrite the existing report data with the most current data |
excel, proc step | source | 5 |
| 270 | Optimal Advertisement Traffic Allocation | Huma Zaidi, Chitta Ranjan |
An ad publisher like eBay has multiple ad sources to serve ads from. Specifically, for eBay text ads, there are two ad sources, viz. eBay Commerce Network (ECN) and Google. The problem and solution we have formulated considers two ad sources. However, the solution can be extended for any number of ad sources. A study was done on performance of ECN and Google ad sources with respect to the revenue per mille (RPM, or revenue per thousand impressions) ad queries they bring while serving ads. It was found that ECN performs better for some ad queries and Google performs better for others. Thus, our problem is to optimally allocate ad traffic between the two ad sources to maximize RPM | key performance indicator, linear programming | source | 15 |
| 271 | Optimizing SAS on Red Hat Enterprise Linux (RHEL) 6 and 7 | Barry Marson | Today, companies are increasingly utilizing analytics to discover new revenue and cost-saving opportunities. Many business professionals turn to SAS, a leader in business analytics software and service, to help them improve performance and make better decisions faster. Analytics are also being employed in risk management, fraud detection, life sciences, sports, and many more emerging markets. However, to maximize the value to the business, analytics solutions need to be deployed quickly and cost-effectively, while also providing the ability to readily scale without degrading performance. Of course, in today's demanding environments, where budgets are still shrinking and mandates to reduce carbon footprints are growing, the solution must deliver excellent hardware utilization, power efficiency, and return on investment. To help solve some of these challenges, Red Hat and SAS have collaborated to recommend the best practices for configuring SAS 9 running on Red Hat Enterprise Linux | sas grid, red hat | source | 20 |
| 272 | Outlier Detection Using the Forward Search in SAS/IML Studio | Jos Polfliet | In cooperation with the Joint Research Centre (JRC) of the European Commission, we have implemented a number of innovative techniques to detect outliers. In this paper, we show the power of SAS/IML Studio as an interactive tool for exploring and detecting outliers using customized algorithms that were built from scratch. The JRC uses this for detecting abnormal trade transactions on a large scale. The outliers are detected using the Forward Search, which starts from a (small) central subset in the data and subsequently adds observations that are close to the current subset based on regression (R-student) or multivariate (Mahalanobis distance) output statistics. The implementation of this algorithm and its applications were done in SAS/IML Studio and converted to a macro for use in PROC IML in Base |
outlier, regression analysis, estimate, predictor variable, proc robustreg, covariance, response variable | source zip |
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| 273 | Partial Promotion of SAS Content Tips and Tricks for Business Users | Andrew Howell | Presentation | sas enterprise guide, sas enterprise guide project, sas management console, sas administrator, sas consultant | source | 12 |
| 274 | Pastries, Microbreweries, Diamonds and More Small Businesses Can Profit with SAS | Cameron Jagoe, Taylor K. Larkin, Denise J. McManus |
Presentation | sas enterprise guide | source | 12 |
| 275 | Pedal-to-the-Metal Analytics with SAS Studio, SAS Visual Analytics, and SAS Visual Statistics | Manuel Figallo-Monge | What is it about SAS Analytics that makes it so analytic? SAS provides a suite of web technologies and applications that aid in the discovery and communication of meaningful data patterns to gain insights. One way to accelerate these insights is through "pedal-to-the-metal analytics" or web-based rapid prototypes; this is because SAS web technologies and applications are closely integrated with one another and, further, can integrate with other systems (such as API-based data sources) through the use of SAS software components called SAS macros that are introduced in this paper. Examples of SAS web applications include SAS Studio, SAS Visual Analytics, and SAS Visual Statistics | sas studio, macro definition, correlation, bubble plot, unstructured data, macro function, score code, regression analysis, data wrangling | source | 20 |
| 276 | Performance Issues and Solutions: SAS with Hadoop | Anjan Matlapudi | Today many analysts in information technology are facing challenges to work with large amount of data. Most analysts are smart enough to find their way out and write quires using appropriate table join conditions, data mining, index keys and hash object for quick data retrieval. Recently SAS system has collaborated with Hadoop data storage and started providing efficient processing power to SAS analytics. In this paper we demonstrate the differences in data retrieval with in the SAS and Hadoop environment. In order to test data retrieval time, we used the following code to randomly generate one hundred million observations with character and numeric variables using RANUIN func tion. DO LOOP=1 to 10e7 will generate ten million records however this code can generate any number of records by changing exponential log. We utilize most commonly used functions and procedure to retrieve records on test dataset and we illustrate real time CPU processing | sas enterprise guide, libname statement, cpu time, proc sql, proc step | source | 8 |
| 277 | Performance Measures Presented with SAS Visual Analytics | Bill Schneider | Presentation | trend analysis, business intelligence | source | 7 |
| 278 | Performing Efficient Wide-to-Long Transposes on Teradata Tables Using SAS Explicit Pass-Through | Tao Cheng | SAS provides in-database processing technology in PROC SQL, which allows the SQL explicit pass- through method to push some or all of the work to a database management system (DBMS). This paper focuses on using the SAS SQL explicit pass-through method to transform Teradata table columns into rows. There are two common approaches for transforming ta ble columns into rows. The first approach is to create 'narrow' tables, one for each column that requires transposition, and then use UNION or UNION ALL to append all the tables together. This approac h is straightforward but can be quite cumbersome, especially when there is a large number of columns that need to be transposed. The second approach is using the Teradata TD_UNPIVOT function which mak es the wide-to-long table transposition an easy job. However, TD_UNPIVOT only allows you to transpo se columns with same data type from wide to long |
macro variable, proc sql, sql pass-through, %if-%then-%else, proc step, create table | source | 13 |
| 279 | Performing Pattern Matching by Using Perl Regular Expressions | Arthur X. Li | SAS provides many DATA STEP functions to search and extract patterns from a character string, such as SUBSTR, SCAN, INDEX, TRANWRD, etc. Using these functions to perform pattern matching often requires utilizing many function calls to match a character position. However, using the Perl Regular Expression (PRX) functions or routines in the DATA STEP will improve pattern matching tasks by reducing the number of function calls and making the program easier to maintain. In this talk, in addition to learning the syntax of Perl Regular Expressions, many real- world applications will be demonstrated |
numeric variable, prxparse, prxchange, prxmatch, sas log, substr function, cpu time, sas output, numeric constant, prxposn, character constant | source zip |
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| 280 | Personal Lending: Customer Credit and Pricing Optimization | Yuri Medvedev | Financial institutions are working hard to optimize credit and pricing strategies for both adjudication and ongoing customer account management. Intense competitive pressures have generated increased revenue challenges for financial institutions. In response to these forces arising within the industry, there is a significant demand to improve the sophistication of methods to manage both the credit exposure and pricing. Numerous credit and pricing optimization applications are available on the market to satisfy these needs. We present a relatively new approach that applies an effect modeling technique on continuous target metrics. The effect modeling method (also referred to as uplift or net lift) can be applied to various continuous targets including revenue, cost, losses, or profit, Examples of effect modeling to optimize the impact of marketing campaigns are known. See Radcliffe and Surry (2011) for a history and literature review | proc hpsplit, interest rate, macro definition, financial services, phase i, treatment effect, confidence interval, phase iii, phase ii, gain chart, data cleaning | source | 19 |
| 281 | Portfolio Optimization with Discontinuous Constraint | Taras Zlupko, Robert Spatz |
Optimization models require continuous constraints to converge. However, some real life problems are better described by models which incorporate discontinuous constraint s. A common type of such discontinuous constraint s becomes apparent when a regulation-mandated diversification requirement is implemented in investment portfolio model. Generally stated the requirement postulates that the aggregate of investments with individual weight s exceeding certain threshold in the portfolio should not exceed some predefined total within the portfolio. This form at of the diversification requirement may be defined by the rules of any specific portfolio construction methodology and is common ly imposed by the regulators. The paper discusses the impact of this type of discontinuous portfolio diversification constraint on the portfolio optimization model solution process and develops a convergent approach | odds ratio, proc step | source zip presentation |
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| 282 | Predicting Forest Fire Occurrence and Incremental Fire Rate Using SAS 9.4 and SAS Enterprise Miner 14.1 | Quyen Nguyen | Fast detection of forest fires is a great concern among environmental experts and national park managers because forest fires create economic and ecological damages and endanger human lives. For effective fire control and resource preparation, it is necessary to predict fire occurrences in advance and estimate the possible losses caused by fires. For this purpose, real -time sensor data of weather conditions and fire occurrence are highly recommended to use in order to support the predicting mechanism. The objective of this study is to use SAS 9.4 and SAS Enterprise Miner 14.1 to predict the probability of fires and to figure out special weather conditions resulting in incremental burned areas in Montesinho Park forest (Portugal). The data set was obtained from the Center for Machine Learning and Intelligent Systems at University of California, Irvine and contains 517 observations and 13 variables from January 2000 to December 2003 | data cleaning, hypothesis testing, confidence interval, proc glm, data mining, predictor variable, validation set, proc genmod, proc step, training set, support vector machine, response variable, likelihood function, binary variable | source | 12 |
| 283 | Predicting Human Activity Sensor Data Using an Auto Neural Model with Stepwise Logistic Regression Inputs | Venkata Vennelakanti | Due to advances in medical care and rise in living standards, life expectancy, on an average increased to 79 years in US. This resulted in the increase of aging population and increase in demand for development of technologies to aid elderly people to live independently and safely. It is possible to achieve this challenge through Ambient-assisted living (AAL) technologies that help elderly people to live more independently. Much research has been done on Human Activity Recognition (HAR) in the last decade and this research work can be used in development of assistive technologies and HAR is expected to be future technology for e-health systems. In this research, I discuss the need to predict human activity accurately by building various models in SAS Enterprise Miner 14.1, on a free public domain dataset, which contains 165,633 observations and 19 attributes | logistic regression, logistic distribution, regression analysis, data cleaning, validation set, proc hpsplit, response variable, training set, neural network | source presentation |
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| 284 | Predicting If Mental Health Facilities Will Offer Free Treatment to Patients Who Cannot Afford It | Ram Poudel, Lynn Xiang |
There is a growing recognition that mental health is a vital public health and development issue worldwide. Considerable studies have reported that there are close interactions between poverty and mental illness. The association between mental illness and poverty is cyclic and negative. Impoverished people are generally more prone to mental illness and are less capable to afford treatment. Likewise, people with mental health problems are more likely to be in poverty. The availability of free mental health treatment to people in poverty is critical to break this vicious cycle. Based on this hypothesis, a model was developed based on the responses provided by mental health facilities to a federally supported survey. We examined if we can predict whether the mental health facilities will offer free treatment to the patients who cannot afford treatment costs in the United States | neural network, proc hpsplit, network optimization, feature selection, data mining, sample, missing value, regression analysis, data cleaning, proc means | source | 11 |
| 285 | Predicting Occurrence Rate of Systemic Lupus Erythematosus and Rheumatoid Arthritis in Pregnant Women | Ravikrishna Vijaya Kumar, Shrie Raam Sathyanarayanan |
Years ago, doctors advised women with autoimmune disease s such as systemic lupus e rythematosus (SLE) and rheumatoid arthritis (RA) not to become pregnant for fear of maternal health. Now, it is known that healthy pregnancy is possible for women with lupus but at the expense of higher pregnancy complication rate. The main objective of this research is to identify key factors contributing to these diseases and to predict the occurrence rate of SLE and RA in pregnant women. Based on the approach used in this study, prediction of adverse pregnancy outcomes for women with SLE, RA and other diseases such as DM (Diabetes Mellitus) and APS (Anti-Phospholipid Antibody Syndrome) can be carried out. These results will help pregnant women to undergo healthy pregnancy by proper medication at an earlier stage. The data set was obtained from Cerner Health Facts data warehouse | proc hpsplit, missing value, regression analysis, feature selection, data cleaning, proc reg, sample, proc means | source | 15 |
| 286 | Predicting Rare Events Using Specialized Sampling Techniques in SAS | Rhupesh Damodaran Ganesh Kumar, Kiren Raj Mohan Jagan Mohan |
In recent years, many companies are trying to understand the rare events that are very critical in the current business environment. But a data set with rare events is always unbalanced and the models developed using this data set cannot predict the rare events precisely. Therefore, to overcome this issue, a data set needs to be sampled using specialized sampling techniques like over -sampling, under- sampling, or the synthetic minority over-sampling technique (SMOTE). The over-sampling technique deals with randomly duplicating minority class observations, but this technique might bias the results. The under-sampling technique deals with randomly deleting majority class observations, but this technique might lose information. SMOTE sampling deals with creating new synthetic minority observations instead of duplicating minority class observations or deleting the majority class observations | sample, data mining, proc step, sas enterprise guide, ks statistic, proc freq, proc hpsplit, gradient boosting, response variable | source | 7 |
| 287 | Predicting response time for the first reply after the question is posted in SAS community forum | Praveen Kumar Kotekal | Presentation | source | 4 | |
| 288 | Predicting the Current Market Value of a Housing Unit across the Four Census Regions of the United States Using SAS Enterprise Miner 12.3 | Mostakim Tanjil, Goutam Chakraborty |
In early 2006, the United States experienced a housing bubble that affected over half of the American states. It was one of the leading causes of the 2007-2008 financial recession. Primarily, the overvaluation of housing units resulted in foreclosures and prolonged unemployment dur ing and after the recession period. The main objective of this study is to predict the current market value of a housing unit with respect to fair market rent, census region, metropolitan statistical area, area median income, household income, poverty income, number of units in the building, number of bedrooms in the unit, utility costs, other costs of the unit, and so on, to determine which factors affect the market value of the housing unit. For the purpose of this study, data was collected from the Housing Affordability Data System of the US Department of Housing and Urban Development. The data set contains 20 variables and 36,675 observations | neural network, regression analysis, proc hpsplit, feature selection, numeric variable, stepwise selection, outlier, mahalanobis distance, data cleaning, sample, proc princomp | source presentation |
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| 289 | Predicting the influence of demographics on Domestic Violence Using SAS Enterprise Guide 6.1 , | Soumil Mukherjee | Presentation | source | 4 | |
| 290 | Preparing for a SAS 9.4 Deployment and Managing that Deployment Throughout Its Lifecycle | Alec S. Fernandez | SAS 9.4 allows for extensive customization of configuration settings. These settings are changed as new products are added into a deployment and upgrades to existing products are deployed into the SAS infrastructure. The ability to track which configuration settings change during the addition of a specific product or the installation of a particular platform maintenance release can be very useful. Often, customers run a SAS deployment step and wonder what exactly changed and in which files. The use of version control systems is becoming increasingly popular for tracking configuration settings. This paper demonstrates how to use Git, a tremendously popular, open-source, version-control system to manage, track, audit, and revert changes to your SAS configuration infrastructure. Using Git, you can quickly list which files were changed by the addition of a product or maintenance package and inspect the differences. You can then revert to the previous settings if that becomes desirable | sas administrator, libname statement | source | 11 |
| 291 | Prescriptive Analytics - Providing the Instruction to Do What's Right | Tho H. Nguyen, Fiona McNeill |
Automation of everyday activities holds the promise of consistency, accuracy and relevancy. When applied to business operations, the additional benefits of governance, adaptability and risk avoidance are realized. Prescriptive analytic empowers both systems and front-line workers to take the desired company action - each and every time. And with data streaming from transactional systems, from IoT, and any other source - doing the right thing with exceptional processing speed embodies the responsive necessity that customers depend on. This talk will describe how SAS and Teradata are enabling prescriptive analytics - in current business environments and in the emerging IoT | prescriptive analytics, data validation rules, big data, sas model manager, if-then-else, data science, data mining | source presentation |
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| 292 | Probability Density for Repeated Events | Bruce S. Lund | In customer relationship management (CRM) or consumer finance it is important to predict the time of repeated events. These repeated events might be a purchase, service visit, or late payment. Specifically, the goal is to find the probability density for the time to first event, the probability density for the time to second event, etc. Two approaches are presented and contrasted. One approach uses discrete time hazard modeling (DTHM). The second, a distinctly different approach, uses multinomial logistic model (MLM). Both DTHM and MLM satisfy an important consistency criterion which is to provide probability densities whose average across all customers equals the empirical population average. DTHM requires fewer models if the number of repeated events is small. MLM requires fewer models if the time horizon is small. SAS code is provided through a simulation in order to illustrate the two approaches | logistic distribution, proc step, logistic regression, proc logistic, binary variable, predictor variable, sample, response variable, regression analysis, likelihood function | source | 16 |
| 293 | PROC DATASETS: The Swiss Army Knife of SAS Procedures | Michael A. Raithel | This paper highlights many of the major capabilities of PROC DATASETS. It discusses how it can be used as a tool to update variable information in a SAS data set; provide information on data set and catalog contents; delete data sets, catalogs, and indexes; repair damaged SAS data sets; rename files; create and manage audit trails; add, delete, and modify passwords; add and delete integrity constraints; and more. The paper contains examples of the various uses of PROC DATASETS that programmers can cut and paste into their own programs as a starting point. After reading this paper, a SAS programmer will have practical knowledge of the many different facets of this important SAS procedure |
dataset, proc step, constraint, audit trail, run, key, libname statement, foreign key, sas library, proc datasets, modify statement, sas log, proc contents, sas programmer, proc sql, attrib statement, data integrity, proc append | source | 25 |
| 294 | PROC DS2 with Both Hands on the Wheel | Peter W. Eberhardt, Xue Yao |
The DATA STEP has served SAS programmers well over the years, and although it is handy, the new, exciting, and powerful DS2 is a significant alternative to the DATA STEP by introducing an object -oriented programming environment. It enables users to effectively manipulate complex data and efficiently manage the programming through additional data types, programming structure elements, user-defined methods, and shareable packages, as well as threaded execution. This tutorial is developed based on our experiences with getting started wit h DS2 and learning to use it to access, manage, and share data in a scalable and standards-based way. It facilitates SAS users of all levels to easily get started with DS2 and understand its basic functionality by practicing the features of DS2 |
missing value, hash table, macro function, proc step, sas base, macro variable, proc fcmp, sas function, sas programmer, proc sql, parallel processing, return, sysdate9, macro definition, temporary array, market basket analysis, dataset | source | 32 |
| 295 | PROC GLIMMIX as a Teaching and Planning Tool for Experiment Design | Walter W. Stroup | In our book, SAS for Mixed Models, (Littell, et al., 2006), we write, "the majority of modeling problems are really design problems." Graduate students and even relatively experienced statistical consultants can find translating a study design into a useful model to be a challenge. Generalized linear mixed models (GLMMs) complicate this challenge, because they accommodate complex designs, complex error structures, and non-Gaussian data. This talk covers strategies proven to be effective in design and modeling courses and consulting sessions. In addition, GLMM methods can be extremely useful in planning experiments. This talk discusses methods to implement precision and power analysis to help choose between competing designs and to assess the adequacy of proposed designs | proc calis, binomial distribution, anova, power analysis, response variable, proc mixed, confidence interval, sample, experimentation, sample size, proc glimmix, covariance, proc step, treatment effect, probability distribution, likelihood function, degrees of freedom, poisson distribution, effect size, normal distribution, standard deviation, numeric functions | source zip |
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| 296 | PROC IMSTAT Boosts Knowledge Discovery in Big-Database (KDBD) in a Pharmaceutical Company | Yoshitake Kitanishi, Ryo Kiguchi, Akio Tsuji, Hideaki Watanabe |
In recent years, Big Data has been in the limelight as a solution for business issues. Big-Data-mining implementations have begun in a variety of industries. The variety of data types and the velocity of increasing data have been astonishing, whether represented as structured data (stored in a relational database) or unstructured data (e.g., text data, GPS data, image data, etc.). In the pharmaceutical industry, Big Data means real-world data, e.g., electronic health records, genomics data, medical imaging data, social network data, etc. Handling these types of Big Data often requires a special environmental infrastructure for statistical computing. This document covers two case studies: 1) the implementation of | proc step, clinical trial, pharmaceutical industry, association rules, big data, clinical data, data science | source | 7 |
| 297 | PROC SQL for SQL Die-hards | Barbara Ross, Mary Jessica Bennett |
Inspired by Christianna William's paper on transitioning to PROC SQL from the DATA STEP, this paper aims to help SQL programmers transition to SAS by using PROC SQL. SAS adapted the Structured Query Language (SQL) by means of PROC SQL back with Version 6. PROC SQL syntax closel y resembles SQL, however, there some SQL features that are not available in SAS. Throughout this paper, we will outline common SQL tasks and how they might differ in PROC SQL; as well as introduce useful SAS features that are not available in SQL. Topics covered are appropriate for novice SAS users |
proc sql, proc step, create table, macro variable, put statement, datetime variable, character variable, date variable, character functions | source | 9 |
| 298 | Processing CDC and SCD Type 2 for Sources without CDC: A Hybrid Approach | Vishant Bhat, Tony Blanch |
In a data warehousing system Change Data capture (CDC) plays an important role not just in making the data warehouse (DWH) aware of the change but also providing a means of flowing the change to the DWH marts and reporting tables so that we see the current and latest version of the truth. This together with Slowly Changing Dimensions (SCD) create a cycle which runs the DWH and provides valuable insights in the history and decision making future when used by machine learning, pattern recognition and forecasting methods. What if the source has no CDC? It would be an ETL nightmare to identify the exact change and report the absolute truth. This hybrid approach shows how to increase the data and metadata capture and quality in data warehouses. Application of this hybrid approach can provide information that is otherwise hidden in areas such as Audit and Fraud detection, while the efficiency provides opportunities for rapidly changing dimensions | dimension table, scd type 2, audit trail, machine learning | source | 8 |
| 299 | Product Purchase Sequence Analyses by Using a Horizontal Data Sorting Technique | Justin Jia, Shan Shan Lin |
Horizontal data sorting is a very useful SAS technique in advanced data analysis using SAS programming. Three years ago (Paper 376-2013), we presented and illustrated various methods and approaches to perform horizontal data sorting, and demonstrated its valuable application in strategic data reporting. However, this technique can also be employed as a creative analytic method in advanced business analytics. This paper presents and discusses its innovative and insightful applications in product purchase sequence analyses, such as product opening sequence analysis, product affinity analysis, next best offer analysis, time span analysis etc. Compared to other analytic approaches, horizontal data sorting technique has the distinct advantages of being straightforward, simple, convenient to use and producing easy-to-interpret analytic results. Therefore the technique can have a wide variety of applications in customer data analysis and business analytics fields | credit card, proc sort, product affinity, data science, frequency analysis, proc freq, proc step, sorted data | source presentation |
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| 300 | Proposing a Recommendation System for DonorsChoose.org | Heramb Joshi, Goutam Chakraborty, Sandeep Chittoor, Vignesh Dhanabal, Sharat Dwibhasi |
recommendation engine, dataset, character variable, data cleaning | source | 21 | |
| 301 | Put Your Data on the Map: Using the GEOCODE and GMAP Procedures to Create Bubble Maps in SAS | Caroline Walker | A bubble map is a useful tool for identifying trends and visualizing the geographic proximity and intensity of events. This paper describes how to use readily available map data sets in SAS along with PROC GEOCODE and PROC GMAP to turn a data s et of addresses and events into a bubble map of the United States with scaled bubbles depicting the location and intensity of event s |
proc step, proc gmap | source | 10 |
| 302 | Quantitle Regression versus Ordinary Least Squares Regression | Ruth Croxford | Regression is used to examine the relationship between one or more explanatory (independent) variables and an outcome (dependent) variable. Ordinary least squares regression models the effect of explanatory variables on the average value of the outcome. Sometimes, we are more interested in modeling the median value or some other quantile (for example, the 10th or 90th percentile). Or, we might wonder if a relationship between explanatory variables and outcome variable is the same for the entire range of the outcome: Perhaps the relationship at small values of the outcome is different from the relationship at large values of the outcome. This presentation illustrates quantile regression, comparing it to ordinary least squares regression. Topics covered will include: a graphical explanation of quantile regression, its assumptions and advantages, using the SAS QUANTREG procedure, and interpretation of the procedure's output |
regression analysis, homoscedasticity, proc quantreg, confidence interval, estimate, response variable, proc reg, proc step, proc glm, proc calis, heteroscedasticity, proc genmod, predictor variable | source | 10 |
| 303 | Rapid Prototyping: Accelerating Development of Your Organization's Reports Using SAS Visual Analytics | Elliot Inman, Michael Drutar |
One of the most important factors driving the success of requirements-gathering can be easily overlooked. Your user community needs to have a clear understanding of what is possible: from different ways to represent a hierarchy to how visualizations can drive an analysis. Discussions about desktop access versus mobile deployment and/or which users might need more advanced statistical reporting can lead to a serious case of option overload. One of the best cures for option overload is to provide your user community with access to template reports they can explore themselves. Instead of beginning requirements-gathering with a blank slate, your users can begin the conversation with, "I would like something like Template #4," greatly reducing the time and effort required to meet their needs. In this paper, we describe how you can take a single rich data set and build a suite of template reports that demonstrate the full functionality of SAS Visual Analytics | dataset, bar chart, time series analysis | source | 19 |
| 304 | Rapid Web Services using SAS/IntrNet Software, jQuery AJAX, and PROC JSON | Jeremy Palbicki | Creating web applications and web services can sometimes be a daunting task. With the ever changing facets of web programming, it can be even more challenging. For example, client-side web programming using applets was very popular almost 20 years ago, only to be replaced with server-side programming techniques. With all of the advancements in JavaScript libraries, it seems client-side programming is again making a comeback. Amidst all of the changing web technologies, surprisingly one of the most powerful tools I have found that has provided exceptional capabilities across all types of web development techniques has been SAS/INTRNET. Traditionally seen as a server -side programming tool for generating complete web pages based on SAS content, SAS/INTRNET, coupled with jQuery AJAX or AJAX alone, also has the ability to provide client-side web programming techniques, as well as provide RESTful web service implementations |
web service, proc json, dataset, proc step | source | 7 |
| 305 | Reading JSON in SAS Using Groovy | John Kennedy | JavaScript Object Notation (JSON) has quickly become the defacto standard for data transfer on the Internet, due to an increase in web data and the usage of fullstack JavaScript. JSON has become dominant in the emerging technologies of the web today, such as the Internet of Things and the mobile cloud. JSON offers a light and flexible format for data transfer, and can be processed directly from JavaScript, without the need for an external parser. SAS has no native implementation for JSON reading; however, the addition of the GROOVY Procedure in SAS 9.3 allows for execution of Java code from within SAS, allowing for JSON data to be read into a SAS dataset. This paper demonstrates the method for parsing JSON into datasets with Groovy and the XML LIBNAME Engine, all within Base |
proc groovy, proc step | source zip |
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| 306 | Real-Time Predictive Modeling of Key Quality Characteristics Using Regularized Regression: SAS Procedures GLMSELECT and LASSO | Jon M. Lindenauer | This paper describes the merging of designed experiments and regularized regression to find the significant factors to use for a real-time predictive model. The real-time predictive model is used in a Weyerhaeuser modified fiber mill to estimate the value of several key quality characteristics. The modified fiber product is accepted or rejected based on the model prediction or the predicted values deviation from lab tests. To develop the model, a designed experiment was needed at the mill. The experiment was planned by a team of engineers, managers, operators, lab techs, and a statistician. The data analysis used the actual values of the process variables manipulated in the designed experiment. There were instances of the set point not being achieved or maintained for the duration of the run. The lab tests of the key quality characteristics were used as the responses | data cleaning, cross-validation, regression analysis, feature selection, sample, series plot, time series analysis, proc glmselect, laboratory data, estimate | source | 10 |
| 307 | Redesigning Control Using the GMATCH Algorithm to Isolate Impact of a Specific Marketing Intervention from Overlapping Solicitations | Mou Dutta, Arjun Natarajan |
The success of any marketing promotion is measured by the incremental response and revenue generated by the targeted population known as Test in comparison with the holdout sample known as Control. An unbiased random Test and Control sampling ensures that the incremental revenue is in fact driven by the marketing intervention. However, isolating the true incremental effect of any particular marketing interve ntion becomes increasingly challenging in the face of overlapping marketing solicitations. This paper demonstrates how a look -alike model can be applied using the GMATCH algorithm on a SAS platform to design a truly comparable control group to accura tely measure and isolate the impact of a specific marketing intervention | sample, random sampling | source zip |
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| 308 | Reducing and Screening Redundant Variables in Logistic Regression Models Using SAS/STAT Software and SAS Enterprise Miner | Xinghe Lu | Logistic r egression is one of the popular regression models in statist ics. It measures the relationship between categorical depend variable and independent variable(s) and predicts the likelihood of having the event associated with outcome variable. Variable reduction and screening are the techniques that reduce the redundant independent variables and filter out the variable(s) with less predictive power. Variable reduction and screening are important and critical for building a reasonable statistical model, especially when dealing with hundreds or thousands of independent variables. This is b ecause they can 1) decrease the model estimation time, 2) avoid occurrence of non-convergence in the model, 3) stabilize the parameter estimate, and 4) decrease the chance of over-fitting. This paper will explore three variable reduction approaches: Effect-selection method, single logistic regressions method and variable clustering method using SAS/STAT software |
proc varclus, logistic regression, feature selection, response variable, proc hpsplit, logistic distribution, regression analysis, data mining, correlation, proc step, cluster analysis, proc varreduce, spearman correlation, predictor variable, nominal variable, proc princomp | source | 10 |
| 309 | Reducing Credit Union Member Attrition with Predictive Analytics | Nathaniel Derby, Mark Keintz |
As credit unions market themselves to increase their market share against the big banks, they understandably focus on gaining new members. However, they must also retain (and further engage) their existing members. Otherwise, the new members they gain can easily be offset by existing members who leave. Happily, by using predictive analytics as described in this paper, it can actually be much easier and less expensive to keep (and further cultivate) existing members than to enlist new ones. This paper provides a step-by-step overview of a relatively simple but comprehensive approach to reduce member attrition. We first prepare the data for a statistical analysis. With some basic predictive analytics techniques, we can then identify those members who have the highest chance of leaving and the highest value. For each of these members, we can also identify why they would leave, thus suggesting the best way to intervene to retain them | data cleaning, gain chart | source | 15 |
| 310 | Reeling Them Back In--Keeping SAS Visual Analytics Users Happy, Behind the Scenes | David W. Franklin | SAS Visual Analytics offers many new and exciting ways to look at your data. Users are able to load data at will to explore and ask questions of their data. But what happens if you need to prevent users from viewing all data? What can you do to prevent users from loading too much data? What happens if a user loads data that exceeds the hardware capacity of your environment? This session covers practical ways to limit available resources and secure SAS LASR Analytic Server data. Real-world scenarios are covered and attendees can participate in an open discussion | sas management console, sas base, sas library | source | 22 |
| 311 | Releasing the Power of SAS into Microsoft SharePoint | Xiaogang Tang | SharePoint is a web application framework and platform developed by Microsoft, mostly used for content and document management by midsize businesses and large departments. Linking SAS with SharePoint will combine the power of these two into one. This paper shows users how to send PDF reports and files in other formats (such as EXCEL files, HTML files, JPEG files, zipped files, etc.) from SAS to a SharePoint Document Library. The paper demonstrates how to configure SharePoint Document Library settings to receive files from SAS. A couple of SAS code examples are included to show how to send files from SAS to SharePoint. The paper also introduces a framework for creating data visualization on SharePoint by feeding SAS data into HTML pages on SharePoint. An example on how to create an infographics SharePoint page with data from SAS is also provided |
data visualization, dataset, excel, business intelligence, proc export, data step, raw data, proc step | source zip |
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| 312 | Responsible Gambling Model at Veikkaus | Tero Kallioniemi | Veikkaus is a state-owned gambling and lottery company in Finland that has a national legalized monopoly in gaming. All the profit goes back to the Finnish society (for art, sports, science and culture) and this is done by Finnish Ministry of Education and Culture who distributes the money. In addition to the government's expectations of Veikkaus' profit, the state has also requirements for handling the social problems of gaming such as problem gambling. Gaming business is balancing between these two factors, business goals and responsibility. For the purposes of identifying problem gambling Veikkaus has created a responsible gaming tool, a model, using SAS tools, which is called 'VasA', the letters coming from Finnish words 'Vastuullinen Asiakkuus', 'Responsible Customer Relations' in English | logistic regression, web service | source | 6 |
| 313 | REST At Ease with SAS: How to Use SAS to Get Your REST | Joseph Henry | Representational State Transfer (REST) is being used across the industry for designing networked applications to provide lightweight and powerful alternatives to web services such as SOAP and Web Services Description Language (WSDL). Since REST is based entirely on HTTP, SAS provides everything you need to make REST calls and to process structured and unstructured data alike. This paper takes a look at how some enhancements in the third maintenance release of SAS 9.4 can benefit you in this area. Learn how the HTTP procedure and other SAS language features provide everything you need to simply and securely use REST |
proc http, proc step, web service, macro variable | source | 7 |
| 314 | Restricted Cubic Spline Regression: A Brief Introduction | Ruth Croxford | Sometimes, the relationship between an outcome (dependent) variable and the explanatory (independent) variable(s) is not linear. Restricted cubic splines are a way of testing the hypothesis that the relationship is not linear or summarizing a relationship that is too non-linear to be usefully summarized by a linear relationship. Restricted cubic splines are just a transformation of an independent variable. Thus, they can be used not only in ordinary least squares regression, but also in logistic regression, survival analysis, and so on. The range of values of the independent variable is split up, with "knots" defining the end of one segment and the start of the next. Separate curves are fit to each segment. Overall, the splines are defined so that the resulting fitted curve is smooth and continuous. This presentation describes when splines might be used, how the splines are defined, choice of knots, and interpretation of the regression results | regression analysis, restricted cubic spline, interval variable, degrees of freedom, proc reg, sample, logistic distribution | source | 5 |
| 315 | Risk Adjustment Methods in Value-Based Reimbursement Strategies | Daryl Wansink | With the move to value-based benefit and reimbursement models, it is essential to quantify the relative cost, quality, and outcome of a service. Accurately measuring the cost and quality of doctors, practices, and health systems is critical when you are developing a tiered network, a shared savings program, or a pay-for-performance incentive. Limitations in claims payment systems require developing methodological and statistical techniques to improve the validity and reliability of provider's scores on cost and quality of care. This talk discusses several key concepts in the development of a measurement system for provider performance, including measure selection, risk adjustment methods, and peer group benchmark development | risk adjustment, insurance claims, confidence interval, correlation, sample | source | 6 |
| 316 | Running Projects for the Average Joe | Jennifer Davies | This paper explores some proven methods used to automate complex SAS Enterprise Guide projects so that the average Joe can run them with little or no prior experience. There are often times when a programmer is requested to extract data and dump it into Microsoft EXCEL for a user. Often these data extracts are very similar and can be run with previously saved code. However, the user quite often has to wait for the programmer to have the time to simply run the code. By automating the code, the programmer regains control over their data requests. This paper discusses the benefits of establishing macro variables and creating stored procedures, among other tips |
sas enterprise guide, stored procedure, macro variable, excel | source | 14 |
| 317 | Running Text Analytics Models in Hadoop | David Bultman, Adam Pilz |
Hadoop took the world by storm as a cost-efficient software framework for storing data. Today, enterprises store large amounts of documents or big data, representing various aspects of their business in Hadoop for later analysis. Information Technology (IT) professionals are averse to moving this data out of Hadoop for the purpose of applying analytical models due to excessive network resource consumption. SAS recognizes that Hadoop is more than just a data store; it's an analytics platform. In this paper, we demonstrate how IT professionals can deploy and run text analytics models, generated by SAS Contextual Analysis, in Hadoop | score code, sas base, libname statement, sas studio, proc step, set statement, macro variable | source zip |
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| 318 | Sampling in SAS using PROC SURVEYSELECT | Rachael Becker, Drew Doyle |
This paper examines the various sampling options that are available in SAS through PROC SURVEYSELECT. We will not be covering all of the possible sampling methods or options that SURVEYSELECT features. Instead we will look at Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Sampling, and Sequential Random Sampling |
sample, random sampling, proc step, cluster sampling, proc surveyselect, sample size, systematic sampling, stratified sampling | source presentation |
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| 319 | SAS & R - A PERFECT COMBINATION | Matthew B. Collins, Taylor K. Larkin |
Presentation | source | 6 | |
| 320 | SAS Analytics for Multi-armed Bandit Market Analysis | Thomas Lehman | This presentation describes the new SAS Customer Intelligence 360 solution and its automated analytic test functions. The new SAS solution provides three automated testing methods; Traditional A/B testing, Multi-Armed Bandit Testing and Multi-Variant Testing. This paper will focus primarily on multi-armed bandit (MAB) analysis of controlled experiments. Multi-armed bandit analysis has been in existence since the 1950's and is sometimes referred to as the K-or N-armed bandit problem where a gambler at a row of slot machines (sometimes known as "one-armed bandits") has to decide which machines to play, as well as the frequency and order to play each machine with the objective of maximizing returns. In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization | proc freq, a/b testing | source | 5 |
| 321 | SAS and Hadoop: The 5th Annual State of the Union | Paul Kent | The third maintenance release of SAS 9.4 (aka SAS 94m3) was a huge release with respect to the interoperability between SAS and Hadoop -- the industry standard for big data. This talk brings you up-to-date with where we are: more distributions, more data types, more options. Come and learn about the exciting new developments for blending your SAS processing with your shared Hadoop cluster. Grid processing. Check. SAS data sets on HDFS. Check | proc sql, proc step, dataset, high-performance sas | source | 12 |
| 322 | SAS Big Brother | Elena Muriel | If, like the character from George Orwell's novel, you need to control what your users are doing and also need to report on it, then this paper is for you. Using a combination of resources available in SAS 9.4, any administrator can control what users are allowed to perform within SAS, and then can create comprehensive and customized reports detailing what was done. This paper discusses how metadata roles can be used to control users' capabilities. Particular attention is given to the user roles available to SAS Enterprise Guide and the SAS Add-in for Microsoft Office, as well as administrator roles available to the SAS Management Console. Best practices are discussed when it comes to the creation of these roles and how they should be applied to groups and users. The second part of this paper explores how to monitor SAS utilization through SAS Environment Manager | sas enterprise guide, data mart, sas management console, memory usage | source | 13 |
| 323 | SAS Customer Intelligence 360: Creating a Consistent Customer Experience in an Omni-channel Environment | Mark Brown, Brian Chick |
SAS Customer Intelligence 360 enables marketers to create customer journeys using activity maps to delight customers with a consistent experience on digital channels such as web, mobile, and email. Activity maps enable the user to create a customer journey across digital channels and assign conversion measures as individual customers and prospects navigate the customer experience. This paper details how those interactions are constructed in terms of what types of tasks are supported and how those tasks can be related to each other in a visual paradigm with built-in goal splits, analytics, wait periods, and A/B testing. Business examples are provided for common omni-channel problems such as cross-selling and customer segmentation. In addition, we cover the integration of SAS Marketing Automation and other marketing products, which enables sites to leverage their current segments for digital channels | data standard, amazon s3, rest api | source | 16 |
| 324 | SAS Debugging 101 | Kirk Paul Lafler | SAS users are almost always surprised to discover their programs contain bugs. In fact, when asked users will emphatically stand by their programs and logic by saying they are bug free. But, the vast number of experiences along with the realities of writing code says otherwise. Bugs in software can appear anywhere; whether accidentally built into the software by developers, or introduced by programmers when writing code. No matter where the origins of bugs occur, the one thing that all SAS users know is that debugging SAS program errors and warnings can be a daunting, and humbling, task. This presentation explores the world of SAS bugs, providing essential information about the types of bugs, how bugs are created, the symptoms of bugs, and how to locate bugs. Attendees learn how to apply effective techniques to better understand, identify, and repair bugs and enable program code to work as intended | options statement, sas log, data cleaning, hash table, proc step | source | 13 |
| 325 | SAS Global Forum | Bob Matsey | Presentation | sample, data science, data lake, data transformation, data warehouse, proc sql, proc step | source | 37 |
| 326 | SAS Grid Administration Made Simple | Scott Parrish, Paula Kavanagh, Linda Zeng |
Historically, administration of your SAS Grid Manager environment has required interaction with a number of disparate applications including Platform RTM for SAS, SAS Management Console, and command line utilities. With the third maintenance release of SAS 9.4, you can now use SAS Environment Manager for all monitoring and management of your SAS Grid. The new SAS Environment Manager interface gives you the ability to configure the Load Sharing Facility (LSF), manage and monitor high-availability applications, monitor overall SAS Grid health, define event-based alerts, and much, much more through a single, unified, web-based interface | sas grid, proc freq | source | 3 |
| 327 | SAS Grid Architecture Solution Using IBM Hardware | Whayne Rouse, Andrew Scott |
This is an in-depth review of SAS Grid performance on IBM Power8 Compute with IBM and Pure Storage solutions. This review spans our environment's growth over the last four years and includes the latest upgrade to our environment from the first maintenance release of SAS 9.3 to the third maintenance release of SAS 9.4 along with a hardware refresh. Originally this environment was hosted on IBM compute with EMC storage and a monolithic installation of SAS 9.2. This environment moved to a SAS 9.3 Grid based solution in January, 2013. And now in 2016 we are launching SAS 9.4 Grid on IBM Power8 Compute with a mix of IBM and PureStorage storage | data mining, data migration | source | 11 |
| 328 | SAS Macro for Generalized Method of Moments Estimation for Longitudinal Data with Time-Dependent Covariates | Katherine Cai, Jeffrey Wilson |
Longitudinal data with time-dependent covariates is not readily analyzed as there are inherent, complex correlations due to the repeated measurements on the sampling unit and the feedback process between the covariates in one time period and the response in another. A generalized method of moments (GMM) logistic regression model (Lalonde, Wilson, and Yin 2014) is one method to analyze such correlated binary data. While GMM can account for the correlation due to both of these factors, it is imperative to identify the appropriate estimating equations in the model. Cai and Wilson (2015) developed a SAS macro using SAS/IML to fit GMM logistic regression models with extended classifications. In this paper we expand the use of this macro to allow for continuous responses and as many repeated time points and predictors as possible. We demonstrate the use of the macro through two examples, one with binary response and another with continuous response |
regression analysis, correlation, logistic regression, time-dependent covariate, logistic distribution, longitudinal data, proc genmod, estimate, proc step, proc reg, dataset, significance level, macro definition, generalized estimating equations, response variable, binary variable, imputation | source | 9 |
| 329 | SAS Metadata Security 101: A Primer for SAS Administrators and Users Not Familiar with SAS | Charyn Faenza | The purpose behind this paper is to provide a high-level overview of how SAS security works in a way that can be communicated to both SAS administrators and users who are not familiar with SAS. It is not uncommon to hear SAS administrators complain that their IT department and users just don't "get it" when it comes to metadata and security. For the administrator or user not familiar with SAS, understanding how SAS interacts with the operating system, the file system, external databases, and users can be confusing. Based on a SAS Enterprise Office Analytics installation in a Windows environment, this paper walks the reader through all of the basic metadata relationships and how they are created, thus unraveling the mystery of how the host system, external databases, and SAS work together to provide users what they need, while reliably enforcing the appropriate security | sas enterprise guide, sas library, libname statement, sas base, sas administrator | source | 12 |
| 330 | SAS Metadata Security 201: Security Basics for a New SAS Administrator | Charyn Faenza | The purpose of this paper is to provide an overview of SAS metadata security for new or inexperienced SAS administrators. The focus of the discussion is on identifying the most common metadata security objects such as access control entries (ACEs), access control templates (ACTs), metadata folders, authentication domains, etc. and describing how these objects work together to secure the SAS environment. Based on a standard SAS Enterprise Office Analytics for Midsize Business installation in a Windows environment, this paper walks through a simple example of securing a metadata environment, which demonstrates how security is prioritized, the impact of each security layer, and how conflicts are resolved | access control, sas management console, sas enterprise guide, sas administrator, sas library | source | 16 |
| 331 | SAS Mobile Analytics: Accelerate Analytical Insights on the Go | Murali Nori | Mobile devices are an integral part of business professional life. Mobile devices are getting increasingly powerful in processor speeds and memory capabilities. Business users can benefit from more analytical visualization of the data with a business context. There are many enhancements in the new SAS mobile BI to facilitate the use SAS Analytics from the new and enhanced SAS Visual Analytics | correlation, line chart | source | 7 |
| 332 | SAS Spontaneous Combustion: Securing Software Portability through Self-Extracting Code | Troy Martin Hughes | Spontaneous combustion describes combustion that occurs without an external ignition source. With the right combination of fire tetrahedron components-including fuel, oxidizer, heat, and chemical reaction-it can be a deadly yet awe-inspiring phenomenon, and differs from traditional combustion that requires a fire source, such as a match, flint, or spark plugs (in the case of combustion engines.) SAS code as well often requires a "spark" the first time it is run or run within a new environment. Thus, SAS programs may operate correctly in an organization's original development environment, but may fail in its production environment until folders are created, SAS libraries are assigned, control tables are constructed, or configuration files are built or modified. And, if software portability is problematic within a single organization, imagine the complexities that exist when SAS code is imported from a blog, white paper, textbook, or other external source into one's own infrastructure | sas library, libname statement, data validation rules, macro variable, data cleaning | source | 11 |
| 333 | SAS Visual Analytics and SAS/ACCESS Interface for Hadoop: Improving Efficiency and Increasing Analyst Satisfaction | Rosie Poultney | Over the past two years, the Analytics group at 89 Degrees has completely overhauled the toolset we use in our day-to-day work. We implemented SAS Visual Analytics, initially to reduce the time to create new reports, and then to increase access to the data so that users not familiar with SAS create their own explorations and reports. SAS Visual Analytics has become a collaboration tool between our analysts and their business partners, with proportionally more time spent on the interpretation. We show an example of this in this presentation. Flush with success, we decided to tackle another area where processing times were longer than we would like, namely weblog data | logistic distribution, regression analysis | source | 10 |
| 334 | SAS Visual Statistics 8.1: The New Self-Service, Easy Analytics Experience | Xiangxiang Meng, Donald R. Chapman, Cheryl LeSaint |
In today's Business Intelligence world, self-service, which allows an everyday knowledge worker to explore data and personalize business reports without being tech-savvy, is a prerequisite. The new release of SAS Visual Statistics introduces an HTML5-based, easy-to-use user interface that combines statistical modeling, business reporting, and mobile sharing into a one-stop self-service shop. The backbone analytic server of SAS Visual Statistics is also updated, allowing an end user to analyze data of various sizes in the cloud. The paper illustrates this new self-service modeling experience in SAS Visual Statistics using telecom churn data, including the steps of identifying distinct user subgroups using decision tree, building and tuning regression models, designing business reports for customer churn, and sharing the final modeling outcome on a mobile device | logistic regression, proc hpsplit, regression analysis, logistic distribution, bar chart, business intelligence, feature engineering, scatter plot, score code, data science, response variable | source | 11 |
| 335 | SAS-IO, a Browser-Based Tool to Automate Creation of Inputs/Outputs Lists for SAS Programs | Mohammad Reza Rezai | High-quality documentation of SAS code is standard practice in multi-user environments for smoother group collaborations. One of the documentation items that facilitate program sharing and retrospective review is a header section at the beginning of a SAS program highlighting the main features of the program such as the program's name, creation date, program's aims, the programmer's identification and project title. In this header section, one of helpful components is to keep a list of the inputs and outputs of the SAS program (e.g. SAS datasets and files the program used and created). This paper introduces SAS-IO, a browser based HTML/JavaScript tool that can automate production of such an input/output list. This can save the programmers' time especially when working with long SAS programs | proc step, output delivery system, macro variable, ods rtf, sas enterprise guide, proc import, proc sql, macro definition | source presentation |
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| 336 | SAS: Cataclysmic Damage Power | Taylor K. Larkin, Denise J. McManus |
Presentation | source | 8 | |
| 337 | Secrets of Efficient SAS Coding Techniques | Andrew T. Kuligowski, Swati Agarwal |
Just as there are many ways to solve any problem in any facet of life, most SAS programming problems have more than one potential solution. Each solution has tradeoffs; a complex program may execute very quickly but prove challenging to maintain, while a program designed for ease of use may require more resources for development, execution, and maintenance. Too often, it seems like those tasked to produce the results are advised on delivery date and estimated development time in advance, but are given no guidelines as to efficiency expectations. The purpose of this paper is to provide ways for improving the efficiency of your SAS programs. It suggests coding techniques, provides guidelines for their use, and shows the results of experimentation to compare various coding techniques, with examples of acceptable and improved ways to accomplish the same task | if-then-else, dataset | source | 5 |
| 338 | Security in Public Reports: Combining Strict Row-Level Authorization with Publically Available Reports Using SAS Visual Analytics | Levi Lindblom | How can you give thousands of customers access to the same reports with the same underlying SAS LASR tables and be confident in that they only see the data that belongs to themselves? Row -level security is the functionality in SAS Visual Analytics that solves this and is easy and straight forward in the course examples. The challenge is how to implement row-level security in a complex real-life example with a high number of users and a huge security hierarchy controlling the access rights? This paper explains and evaluates the most common implementation alternatives based on the experience and performance testing from a customer project. It also explains how the selected alternative was implemented | star schema, dataset, proc sql, sample, create view, proc step | source | 13 |
| 339 | Setting Up Winning Conditions for a Migration of SAS from a PC Environment to a Server Environment | Mathieu Gaouette | Whether it is a question of security or a question of centralizing the SAS installation to a server, the need to phase out SAS in the PC environment has never been so common. On the surface, this type of migration seems very simple and smooth. However, migrating SAS from a PC environment to a SAS server environment (SAS Enterprise Guide) is really easy to underestimate. This paper presents a way to set up the winning conditions to achieve this goal without falling into the common traps. Based on a successful conversion with a past employer, I have identified a high-level roadmap with specific | excel, sas enterprise guide | source | 5 |
| 340 | Shuffle Up and Deal! Sampling without Replacement Using SAS | Daniel R. Bretheim | Sampling, whether with or without replacement, is an important component of the hypothesis testing process. In this paper, we demonstrate the mechanics and outcome of sampling without replacement, where sample values are not independent. In other words, what we get in the first sample affects what we can get for the second sample, and so on. We use the popular variant of poker known as No Limit Texas Hold'em to illustrate | sample, do loop, hypothesis testing, numeric variable, point= option, set statement, credit card | source presentation |
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| 341 | Similarity Analysis: an Introduction, a Process, and a Supernova | David J. Corliss | Similarity analysis is used to classify an entire time series by type. In this method, a distance measure is calculated to reflecting difference in form between two ordered sequences, such as are found in time series data. The mutual differences between many sequences o r time series can by calculated in SAS using PROC SIMILARITY and then compiled in a similarity matrix, similar in form to a correlation matrix. When cluster methods are applied to a similarity matrix, time series with similar patterns are grouped together and placed into clusters distinct from others containing times series with differen t patterns. In this example, similarity analysis is used to classify supernovae by way the amount of light they produce changes over time. Additional examples demonstrate the use of similarity analysis in other areas, including biostatistics and econometri cs |
time series analysis, proc step, proc standard, cluster analysis, distance measure, correlation, outlier | source | 11 |
| 342 | Simulation of Imputation Effects Under Different Assumptions | Danny Rithy | Presentation | source | 5 | |
| 343 | Simultaneous Forecasts of Multiple Interrelated Time Series with Markov Chain Model | Gongwei Chen | In forecasting, there are often situations where several time series are interrelated: components of one time series can transition into and from other time series. A Markov chain forecast model may readily capture such intricacies through the estimation of a transition probability matrix, which enables a forecaster to forecast all the interrelated time series simultaneously. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. Implementation of a Markov chain forecast model is straightforward using SAS/IML. This paper demonstrates a real world application in forecasting community supervision caseload in Washington State. A Markov model was used to forecast five interrelated time series in the midst of turbulent caseload changes. This paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Sample code using SAS/IML is provided |
markov chain, time series analysis, sensitivity analysis, sample | source | 11 |
| 344 | Size Optimization Made Simple: Creating Omni-Channel and Supply Chain Efficiencies to Increase Consumer Satisfaction and Revenue | Donna McGuckin | Retailers and wholesalers invest heavily in technology, people, processes, and data to create relevant assortments across channels. While technology and vast amounts of data help localize assortments based on consumer preferences, product attributes, and store performance, it's impossible to complete the assortment planning process down to the most granular level of size. The ability to manage millions of size and store combinations is burdensome, not scalable, and not precise. Valuable time and effort is spent creating detailed, insightful assortments only to marginalize those assortments by applying corporate averages of size selling for the purchasing and distribution of sizes to locations. The result is missed opportunity: disappointed customers, lost revenue, and lost profitability due to missing sizes and markdowns on abundant sizes | customer satisfaction, key performance indicator | source | 11 |
| 345 | Solving a Business Problem with SAS EG Creating an Inpatient Census Model | Thomas Gant | Presentation | sas enterprise guide, sas management console, proc format, proc sql, proc step | source | 8 |
| 346 | Solving the 1,001-Piece Puzzle in 10 (or Fewer) Easy Steps: Using SAS9 .cfg, autoexec.sas, SAS Registry, and Options to Set Up Base SAS | Peter W. Eberhardt | Are you frustrated with manually setting options to control your SAS Display Manager sessions but become daunted every time you look at all the places you can set options and window layouts? In this paper, we look at various files SAS accesses when starting, what can (and cannot) go into them, and what takes precedence after all are executed. We also look at the SAS Registry and how to programmatically change settings. By the end of the paper, you will be comfortable in knowing where to make the changes that best fit your needs | proc step, libname statement | source | 10 |
| 347 | Something Old, Something New... Flexible Reporting with DATA Step-based Tools | Peter J. Lund | The report looks simple enough-a bar chart and a table, like something created with the GCHART and REPORT procedures. But, there are some twists to the reporting requirements that make those procedures not quite flexible enough. The solution was to mix "old" and "new" DATA STEP-based techniques to solve the problem. Annotate datasets are used to create the bar chart and the Report Writing Interface (RWI) to create the table. Without a whole lot of additional code, an extreme amount of flexibility is gained. The goal of this paper is to take a specific example of a couple generic principles of programming (at least in SAS): 1. The tools you choose are not always the most obvious ones - So often, other from habit of comfort level, we get zeroed in on specific tools for reporting tasks |
data step, proc report, proc step, bar chart | source | 15 |
| 348 | Sorting a Bajillion Records: Conquering Scalability in a Big Data World | Troy Martin Hughes | "Big data" is often distinguished as encompassing high volume, velocity, or variability of data. While big data can signal big business intelligence and big business value, it also can wreak havoc on systems and software ill-prepared for its profundity. Scalability describes the ability of a system or software to adequately meet the needs of additional users or its ability to utilize additional processors or resources to fulfill those added requirements. Scalability also describes the adequate and efficient response of a system to increased data throughput. Because sorting data is one of the most common as well as resource-intensive operations in any software language, inefficiencies or failures caused by big data often are first observed during sorting routines | proc sort, proc step, hash table, cpu time, proc sql, sample, big data, sas base, character variable, work library, memory usage, exception handling, sorted data, numeric variable, key performance indicator, macro definition, options statement, macro variable, macro facility, macro loop, by group processing, if-then-else, order by, sysrc, sas library, %if-%then-%else, macro put, missing value | source presentation |
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| 349 | Sparking Analytical Insight with SAS Data Loader for Hadoop | Matthew Magne, Scott Gidley |
Sixty percent of organizations will have Hadoop in production by 2016, per a recent TDWI survey. It's become increasingly challenging to access, cleanse, and move these huge volumes of data. How can data scientists and business analysts clean up their data, manage it where it lives, and overcome the big data skills gap? It all comes down to accelerating the data preparation process. SAS Data Loader leverages years of expertise in data quality and data integration, a simplified user interface, and the parallel processing power of Hadoop to overcome the skills gap and compress the time taken to prepare data for analytics or visualization. We cover some of the new capabilities of SAS Data Loader for Hadoop, including in-memory Apache Spark processing of data quality and master data management functions, faster profiling and unstructured data field extraction, and chaining multiple transforms for improved productivity | proc sql, spark, data cleaning, data governance, big data, parallel processing, master data management, rest api | source | 7 |
| 350 | Spatial Dependence, Nonlinear Panel Models, and More New Features in SAS/ETS 14.1 | Jan Chvosta | SAS/ETS 14.1 delivers a substantial number of new features to researchers who want to examine causality with observational data in addition to forecasting the future. This release adds count data models with spatial effects, new linear and nonlinear models for panel data, the X13 procedure for seasonal adjustment, and many more new features. This paper highlights the many enhancements to SAS/ETS software and demonstrates how these features can help your organization increase revenue and enhance productivity |
vecm, estimate, proc calis, regression analysis | source | 11 |
| 351 | Statistical Comparisons of Disease Prevalence Rates Using the Bootstrap Procedure | Matthew Dutton, Charlotte Baker |
Disease prevalence is one of the most basic measures of the burden of disease in the field of epidemiology. As an estimate of the total number of cases of disease in a given population, prevalence is a standard in public health analysis. The prevalence of diseases in a given area is also frequently at the core of governmental policy decisions, charitable organization funding initiatives, and countless other aspects of everyday life. However, all too often, prevalence estimates are restricted to descriptive estimates of population characteristics when they could have a much wider application through the use of inferential statistics. As an estimate based on a sample from a population, disease prevalence can vary based on random fluctuations in that sample rather than true differences in the population characteristic | sample, bootstrap, inferential statistics, proc step, confidence interval, central limit theorem, proc surveyselect, proc univariate, data science, statistic | source | 6 |
| 352 | Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software | Bob Rodriguez | The increasing size and complexity of data in research and business applications require a more versatile set of tools for building explanatory and predictive statistical models. In response to this need, SAS/STAT software continues to add new methods. This paper provides a high-level tour of five modern approaches to model building that are available in recent releases of SAS/STAT: building sparse regression models with the GLMSELECT procedure, building generalized linear models with the HPGENSELECT procedure, building quantile regression models with the QUANTSELECT procedure, fitting generalized additive models with the GAMPL procedure, and building classification and regression trees with the HPSPLIT procedure. The paper reviews the key concepts of each approach and illustrates the syntax and output of each procedure with a basic example |
regression analysis, proc hpgenselect, proc gam, forward selection, proc step, poisson distribution, proc glmselect, proc genmod, significance level, proc hpsplit, backward selection, binomial distribution, proc quantreg, stepwise selection | source | 23 |
| 353 | Stored Processes and SAS Visual Analytics: Giving Users the Power to Load | Renato Luppi, Varsha Chawla |
A stored process is a SAS program that can be executed as required by different applications. Stored processes have been making SAS users' lives easier for decades. In SAS Visual Analytics, stored processes can be used to enhance the user experience, create custom functionality and output, and expand the usefulness of reports. This paper will discuss a technique on how data can be loaded on demand into memory for SAS Visual Analytics and accessed by reports as needed using stored processes. Loading tables into memory so that they can be used to create explorations or reports is a common task in SAS Visual Analytics. This task is usually done by an administrator, enabling the SAS Visual Analytics user to have a seamless transition from data to report. At times, however, users will have a need for tables to be partially loaded or modified in memory on demand | moving average, sas management console, macro variable, dataset, proc step | source | 21 |
| 354 | Strategies for Developing and Delivering an Effective Online SAS Programming Course | Justina M. Flavin | Teaching online courses is very different from teac hing in the classroom setting. Developing and deliv ering an effective online class entails more than just trans ferring traditional course materials into written d ocuments and posting them in a course shell. This paper will dis cuss the author's experience in converting a traditional hands-on introductory SAS programming class into an online course and present some ideas to promote successful learning and knowledge transfer when teaching online | sas programming, missing value, dataset, sas enterprise guide, clinical data, flat file, fisher exact test, proc step, filename statement, exact test, sample, proc freq | source | 11 |
| 355 | Streaming Decisions: How SAS Puts Streaming Data to Work | Fiona McNeill, David R. Duling, Steve Sparano |
Sensors, devices, social conversation streams, web movement, and all things in the Internet of Things (IoT) are transmitting data at unprecedented volumes and rates. SAS Event Stream Processing ingests thousands and even hundreds of millions of data events per second, assessing b oth the content and the value. The benefit to organizations comes from doing something with those results, and eliminating the latencies associated with storing data before analysis happens. This paper bridges the gap. It describes how to use streaming data as a portable, lightweight micro-analytics service for consumption by other applications and systems | stream processing, real-time processing, proc hpsplit, score code, iot, data cleaning, missing value, data validation rules, prescriptive analytics, response variable, sample, data science, likelihood function, data mining | source zip |
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| 356 | Super Boost Data Transpose Puzzle | Ahmed Al-Attar | This paper compares different solutions to a data transpose puzzle presented to the SAS User Group at the US Census Bureau (CenSAS). The presented solutions ranged from a SAS 101 multi-step solution to an advanced solution utilizing not widely known techniques yielding 85% run time savings! | proc step, proc transpose, proc sql | source zip |
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| 357 | Survey Data Imputation with PROC SURVEYIMPUTE | Pushpal K. Mukhopadhyay | Big data, small data-but what about when you have no data? Survey data commonly include missing values due to nonresponse. Adjusting for nonresponse in the analysis stage might lead different analysts to use different, and inconsistent, adjustment methods. To provide the same complete data to all the analysts, you can impute the missing values by replacing them with reasonable nonmissing values. Hot-deck imputation, the most commonly used survey imputation method, replaces the missing values of a nonrespondent unit by the observed values of a respondent unit. In addition to performing traditional cell-based hot-deck imputation, the SURVEYIMPUTE procedure, new in SAS/STAT 14.1, also performs more modern fully efficient fractional imputation (FEFI). FEFI is a variation of hot-deck imputation in which all potential donors in a cell contribute their values. Filling in missing values is only a part of PROC SURVEYIMPUTE |
imputation, proc step, sample, missing value, sample weight, proc freq, proc mianalyze, proc calis, bootstrap, confidence interval | source | 26 |
| 358 | Survival Analysis of the Patients Diagnosed with Non-Small Cell Lung Cancer Using SAS Enterprise Miner 13.1 | Raja Rajeswari Veggalam, Akansha Gupta, Goutam Chakraborty |
Cancer is the second leading cause of deaths in United States. About 85%-90% of all lung cancers are non-small cell lung cancer and they constitute about 26.8% of cancer deaths. An efficient cancer treatment plan is therefore of prime importance for increasing the survival chances of a patient. The cancer treatments are generally given to a patient in multiple sittings and often doctors tend to make the decisions based on improvement over time. Calculating the survival chances of the patient with respect to time and determining the various factors that influence the survival time would help doctors make more informed decisions about the further course of treatment and help patients develop a pro active approach in making choices for the treatment. The objective of this paper is to analyze the surviv al time of patients suffering from non-small cell lung cancer, identify the time interval crucial for the su rvival and identify the effects of various factors such as age, gender and treatment type. The performances of the models built are analyzed to understand the significance of cubic splines used in these models. The dataset is from the CERNER database with 548 records and 12 variables from 2009-2013. The patient records wi th loss to follow up are censored. The survival analysis is performed using parametric and non-parametric methods in SAS Enterprise Miner 13.1. The analysis revealed that the survival probability of a patien t is high within two weeks of hospitalization and the probability of survival goes down by 60% between weeks 5 to 9 of admission. Age and gender play a prominent role in influencing the survival time and risk. The probability of survival for female patients decreases by 70% and 80% during week 6 and week 7 respectively and for male patients' decreases by 70% and 50% during week 8 and week 13 respectively | survival analysis, hazard function, response variable, survival function, data mining, key, stepwise selection, regression analysis, correlation | source | 11 |
| 359 | Tales from the Crypt | Safer Anonymization | Presentation | source | 12 | |
| 360 | Taming the Rule | Charlotte Crain, Chris Upton |
When business rules are deployed and executed--whether a rule is fired or not-if the rule-fire outcomes are not monitored or investigated for validation or challenged, over time unintended business impacts can occur because of changing data profiles or characteristics of the input data for the rules. Comparing scenarios using modified rules and visually comparing how they might impact your business can aide you in meeting compliance regulations, knowing your customers, and staying relevant or accurate in your particular business context. Visual analysis of rules outcomes is a powerful way to validate what is expected or to compare potential impact that could lead to further investigation and refinement of existing rules. This paper will show how to use SAS Visual Analytics and other visualizations to perform various types of meaningful and useful rule-fire outcome analysis with rules that have been created in SAS Business Rules Manager | data validation rules, heatmap, proc hpsplit, credit card, box plot, bar chart | source | 13 |
| 361 | Teaching SAS Using SAS OnDemand for Academics: Studio | Charlotte Baker, C. Perry Brown |
Universities, government agencies, and non-profits all require various levels of data analysis, data manipulation, and data management. This requires a workforce that knows how to use statistical packages. The server-based SAS OnDemand for Academics: Studio offerings are excellent tools for teaching SAS in both postsecondary education and in professional continuing education settings. SAS OnDemand for Academics: Studio can be used to share resources; teach users using Windows, UNIX, and Mac OS computers; and teach in various computing environments. This paper discusses why one might use SAS OnDemand for Academics: Studio instead of SAS University Edition, SAS Enterprise Guide, or Base SAS for education and provides examples of how SAS OnDemand for Academics: Studio has been used for in-person and virtual training | sas enterprise guide, sas base, libname statement, proc step | source | 11 |
| 362 | Ten Tips to Unlock the Power of Hadoop with SAS | Nancy A. Rausch, Wilbram Hazejager |
This paper discusses a set of practical recommendations for optimizing the performance and scalability of your Hadoop system using SAS. Topics include recommendations gleaned from actual deployments from a variety of implementations and distributions. Techniques cover tips for improving performance and working with complex Hadoop technologies such as Kerberos, techniques for im proving efficiency when working with data, methods to better leverage the SAS in Hadoop components, and other recommendations. With this information, you can unlock the power of SAS in your Hadoop system | proc sql, proc step, sas log, create table, big data, libname statement, proc append, hive, in-database processing, sql pass-through | source | 12 |
| 363 | Text Analysis of Hotel Reviews | Saurabh Nandy, Neha Singh |
Presentation | source | 4 | |
| 364 | Text Analytics and Assessing Patient Satisfaction | Divya Sridhar | Presentation: A survey is often the preferred way to obtain information and feedback for program improvement. At a bare minimum, survey research comprises of economics, statistics, and psychology in order to show how surveys measure and predict important aspects of the human condition. Through healthcare-related research papers written by experienced practitioners in the industry, a list of several factors that are important for patients during hospital visits were hypothesized. Through text mining using SAS Enterprise Miner 12.1, tangible aspects of hospital quality and patient care were measured using online survey responses and comments found in the English National Health Service website. Patient comments were used to analyze positive, negative, or mixed feedback and whether it correlates with research done on hospital quality. The implications of this research are vital in comprehending our health care system and the factors that are important in satisfying patient needs | data mining, key performance indicator | source presentation |
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| 365 | Text Analytics and Brand Topic Maps | Nicholas Evangelopoulos | The use of Text Analytics as a tool for strategic analysis of an organization, a leader, a brand, or a process, is examined. Software solutions SAS Enterprise Miner and Base SAS are used to extract topics from text and create visualizations that identify the relative placement of the topics next to various business entities. Two case studies that identify and visualize brand-topic relationships in the context of branding, leadership, and service quality, are presented | proc corresp, proc freq, scree plot, data mining, proc princomp, parsing, proc step | source | 11 |
| 366 | Text Mining Secretary Clinton's Emails | Michael Ames | The recent controversy regarding former Secretary Hillary Clinton's use of a non-government, privately maintained email server provides a great opportunity to analyze real-world data using a variety of analytic techniques. This email corpus is interesting because of the challenges acquiring and preparing the data for analysis as well as the variety of analysis that can be performed, including techniques for searching, entity extraction and resolution, natural language processing for topic generation, and social network analysis. Given the potential for politically charged discussion, rest assured there will be no discussion of politics-just fact-based analysis | data cleaning, proc sql | source | 9 |
| 367 | That's All Right: More Complex Reports | Cynthia L. Zender | Are you living in Heartbreak Hotel because your boss wants different statistics in the SAME column on your report? Need a currency symbol in one cell of your pre-summarized data, but a percent sign in another cell? Large blocks of text on your report have you all shook up because they wrap badly on your report? Have you hit the wall with PROC PRINT? Well, rock out of your jailhouse with ODS, DATA STEP, and PROC REPORT. This paper is a sequel to the popular 2008 paper "Creating Complex Reports." The paper presents a nuts-and-bolts look at more complex report examples gleaned from SAS Community Forum questions and questions from students. Examples will include use of DATA STEP manipulation to produce PROC REPORT and PROC SGPLOT output as well as examples of ODS LAYOUT and the new Report Writing Interface. And PROC TEMPLATE makes a special guest appearance |
proc step, proc report, excel, ods pdf, proc sgplot, output delivery system, proc print, ods html, ods excel, if-then-else, sas programmer, by group processing, macro variable, format statement | source zip |
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| 368 | The Application of Fatality Analysis Reporting System Data on the Road Safety Education of US, DC, and PR Minors | Molly Funk, Max Karsok, Michelle Williams |
sas enterprise guide, cluster analysis | source | 18 | |
| 369 | The Baker's Dozen: What Every Biostatistician Needs to Know | AnnMaria De Mars | It's impossible to know all of SAS or all of statistics. There will always be some technique that you don't know. However, there are a few techniques that anyone in biostatistics should know. If you can calculate those with SAS, life is all the better. In this session you will learn how to compute and interpret a baker's dozen of these techniques, including several statistics that are frequently confused. The following statistics are covered: prevalence, incidence, sensitivity, specificity, attributable fraction, population attributable fraction, risk difference, relative risk, odds ratio, Fisher's exact test, number needed to treat, and McNemar's test. The 13th, extra bonus tool, is SAS statistical graphics. With these 13 tools in their tool chest, even non-statisticians or statisticians who are not specialists will be able to answer many common questions in biostatistics. The fact that each of these can be computed with a few statements in SAS makes the situation all the sweeter | proc step, proc freq, relative risk, odds ratio, fisher exact test, exact test, sample, proc sgplot, proc freq chisq, confidence interval, proc transpose | source | 17 |
| 370 | The Combination of SAS and VBA Makes Life Easier | Zhu Yanrong | VBA has been described as a glue language and has b een extensively used in exchanging data between Microsoft products such as EXCEL and Word or PowerPoint. How to trigger the VBA macro from SAS via DDE has been widely discussed in recent years. Howe ver, using SAS to send parameters to VBA macro was rarely reported. This paper provides a solution for this problem. Copying EXCEL tables to PowerPoint utilizing the combination of SAS and VBA is illustrated as an example. The SAS program rapidly scans a ll EXCEL files contained in one folder, passes the file information to VBA as parameters, and triggers the VBA macro to write PowerPoint files in a loop. As a result, a batch of PowerPoint files can be generat ed by just one single mouse-click |
excel, put statement | source | 5 |
| 371 | The Dynamic Duo: ODS Layout and the ODS Destination for PowerPoint | Jane Eslinger | Like a good pitcher and catcher in baseball, ODS layout and the ODS destination for PowerPoint are a winning combination in SAS 9.4. With this dynamic duo, you can go straight from performing data analysis to creating a quality presentation. The ODS destination for PowerPoint produces native PowerPoint files from your output. When you pair it with ODS layout, you are able to dynamically place your output on each slide. Through code examples this paper shows you how to create a custom title slide, as well as place the desired number of graphs and tables on each slide. Don't be relegated to the sidelines-increase your winning percentage by learning how ODS layout works with the ODS destination for PowerPoint |
proc step, output delivery system, title statement, end, by group processing | source | 17 |
| 372 | The Fantastic Four: Running Your Report Using the TABULATE, TRANSPOSE, REPORT, or SQL Procedure | Joshua M. Horstman | Like all skilled tradespeople, SAS programmers have many tools at their disposal. Part of their expertise lies in knowing when to use each tool. In this paper, we use a simple example to compare several common approaches to generating the requested report: the TABULATE, TRANSPOSE, REPORT, and SQL procedures. We investigate the advantages and disadvantages of each method and consider when applying it might make sense. A variety of factors are examined, including the simplicity, reusability, and extensibility of the code in addition to the opportunities that each method provides for customizing and styling the output. The intended audience is beginning to intermediate SAS programmers | proc step, proc report, proc tabulate, adverse events, proc sql, proc freq, select, proc transpose, sas programmer, class variable, proc template, options statement | source | 13 |
| 373 | The GEOCODE Procedure and SAS Visual Analytics | A. Darrell Massengill | SAS Visual Analytics can display maps with your location information. However, you might need to display locations that do not match the categories found in the SAS Visual Analytics interface, such as street address locations or non- US postal code locations. You might also wish to display custom locations that are specific to your business or industry, such as the locations of power grid substations or railway mile markers. Alternatively, you might want to validate your address data that you are using with SAS Visual Analytics. This paper shows how PROC GEOCODE can be used to simplify geocoding by processing your location information before importing data into SAS Visual Analytics |
proc step, proc print | source zip |
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| 374 | The HPSUMMARY Procedure: An Old Friend's Younger (and Brawnier) Cousin | Anh P. Kellermann, Jeffrey D. Kromrey |
The HPSUMMARY procedure provides data summarization tools to compute basic descriptive statistics for variables in a SAS dataset. It is a high-performance version of the SUMMARY procedure in Base SAS. Although the PROC SUMMARY is popular with data analysts, the PROC HPSUMMARY is still "a new kid on the block." This paper provides an introduction to PROC HPSUMMARY by comparing it with its well-known counterpart, PROC SUMMARY. General syntax differences as well as performance in terms of processing time and memory utilization of the two procedures are examined. Simulated data of different sizes were used to observe the performance of the two procedures. Experiment results indicate that there was no clear difference in real time between the PROC SUMMARY and its high performance counterpart. The HP version utilized more memory but provided better memory management in a limited - memory environment than the legacy version did |
proc step, proc means, cpu time, sas base, memory usage, out=, sample size, sample, output, sas output | source presentation |
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| 375 | The Journey Towards an Omnichannel Reality | Jørn Tronstad | Omni-Channel, and the omniscient customer experience, is most commonly used as a buzzword to describe the seamless customer experience in a traditional multi-channel marketing and sales environment. With more channels and methods of communication, there is a growing need to establish a more customer-centric way of dealing with all customer interactions, not only 1:1. Telenor, based out of Norway, is one of the world's major mobile operators with in excess of 200 million mobile subscriptions throughout 13 markets across Europe and Asia. The Norwegian home-market is a highly saturated and mature market in which customer demands and expectations are constantly rising. To deal with this and with increased competition, two major initiatives were established together with SAS. The initiatives aimed to leverage both the need for real-time analytics and decision management in our inbound channel, and for creating an Omni-Channel experience across inbound and outbound channels | customer experience, data validation rules | source | 10 |
| 376 | The New SAS Map Data Sets | A. Darrell Massengill | SAS customers have a growing need for quality and accurate SAS maps. In order for SAS to provide these quality maps, we need good, solid, and accurate map data. Because SAS is not in the business of generating map data, we have licensed the maps from a third party to satisfy this need. This paper explores the new maps by discussing the problems and limitations with the old map data, along with showing features and examples of using the new map data | proc step, sample, proc gmap | source zip |
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| 377 | The Optimization of the Optimal Customer | Pamela Dixon | For marketers who are responsible for identifying the best customer to target in a campaign, it is often daunting to determine which media channel, offer, or campaign program is the one the customer is more apt to respond to, and therefore, is more likely to increase revenue. This presentation examines the components of designing campaigns to identify promotable segments of customers and to target the optimal customers using SAS Marketing Automation integrated with SAS Marketing Optimization | data validation rules, customer segmentation | source | 9 |
| 378 | The Path Length: Parent-Child De-lineage with PROC TREE and ODS | Can Tongur | The SAS procedure PROC TREE sketches parent-child lineage-also known as trees-from hierarchical data. Hierarchical relationships can be difficult to flatten out into a data set, but with PROC TREE, its accompanying ODS table TREELISTING, and some creative yet simple handcrafting, a de-lineage of parent- children variables can be derived. Because the focus of PROC TREE is to provide the tree structure in graphical form, it does not explicitly output the depth of the tree, although the depth is visualized in the accompanying graph. Perhaps unknown to most, the path length variable, or simply the height of the tree, can be extracted from PROC TREE merely by capturing it from the ODS output, as demonstrated in this paper |
proc tree, proc step, sas output, sample, proc sql | source | 10 |
| 379 | The Roads We Take Let's Hash It Out! | David Izrael, Elizabeth Axelrod |
Presentation | source | 7 | |
| 380 | The SAS Test from H*ll How Well do you REALLY know SAS? | Glen Becker | Presentation | source | 10 | |
| 381 | The Scheduler: Give Your Punch Data a New Dimension | Ariel Kumpinsky | The Scheduler is an innovative tool that maps linear data (i.e., time stamps) to an intuitive 3- dimensional representation. This transformation allows the user to identify relationships, conflicts, gaps, etc. that were not readily apparent in the data's native form. This tool has applications in operations research and litigation related analyses. This paper will discuss why The Scheduler was created, the data available to analyze the issue, and how this code can be used in other types of applications. Spec ifically, this pape r will discuss how The Scheduler can be used by supervisors to maintain the presence of three or more employees at all times, which can help ensure all federal and st ate mandated work breaks are ta ken. Additional examples for The Scheduler include assisting construction foreman to create a schedule that visualizes the presence of contractors in a logical sequence while elimin ating overlap and/or en suring cushions of time between contractors and matching items to non-uniform information | odds ratio, cpu time | source | 7 |
| 382 | The Six Tenets of a Better Decision | Daniel Jahn | SAS helps people make better decisions. But what makes a decision better? How can we make sure we are not making worse decisions? There are six tenets to follow to ensure we are making better decisions. Decisions are better when they are: (1) Aligned with your mission; (2) Complete; (3) Faster; (4) Accurate; (5) Accessible; and (6) Recurring, ongoing, or productionalized. By combining all of these aspects of making a decision, you can have confidence that you are making a better decision. The breadth of SAS software is examined to understand how it can be applied toward these tenets. Scorecards are used to ensure that your business stays aligned with goals. Data Management is used to bring together all of the data you have, to provide complete information. SAS Visual Analytics offerings are unparalleled in their speed to enable you to make faster decisions. Exhaustive testing verifies accuracy | entity resolution, data governance, stream processing, real-time processing, proc freq | source | 9 |
| 383 | The Three I's of SAS Log Messages, IMPORTANT, INTERESTING, and IRRELEVANT | William E. Benjamin Jr. | I like to think that SAS error messages come in three flavors, IMPORTANT, INTERESTING, and IRRELEVANT. SAS calls its messages NOTES, WARNINGS, and ERRORS. I intend to show you that not all NOTES are IRRELEVANT nor are all ERRORS IMPORTANT. This paper will walk through many different scenarios and explain in detail the meaning and impact of messages presented in the SAS log. I will show you how to locate, classify, analyze, and resolve many different SAS message types. And for those brave enough I will go on to teach you how to both generate and suppress messages sent to the SAS log. This pape r presents an overview of messages that can often be found in a SAS Log window or output file. The intent of this presentation is to familiarize you with common messages, the meaning of the messages, and how to determine the best way to react to the messages. Notice I said "react", not necessarily correct. Code examples and log output will be presented to aid in the explanations | error message, sas log, note, libname statement, merge statement, dataset, cpu time, options statement, sample, proc step, macro variable, proc printto, error | source zip |
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| 384 | The Use of Statistical Sampling in Auditing Health-Care Insurance Claim Payments | Taylor Lewis, Julie Johnson, Christine Muha |
This paper is a primer on the practice of designing, selecting, and making inferences on a statistical sample, where the goal is to estimate the magnitude of error in a book value total. Although the concepts and syntax are presented through the lens of an audit of health-care insurance claim payments, they generalize to other contexts. After presenting the fundamental measures of uncertainty that are associated with sample-based estimates, we outline a few methods to estimate the sample size necessary to achieve a targeted precision threshold. The benefits of stratification are also explained. Finally, we compare several viable estimators to quantify a book value discrepancy, making note of the scenarios where one might be preferred over the others | sample, sample size, proc calis, proc step, parameter, proc surveymeans, regression analysis, proc surveyselect, population, insurance claims, standard deviation, random sampling, response variable, sas syntax, sample weight | source | 18 |
| 385 | Three Methods to Dynamically Assign Colors to Plots Based on Group Value | Amos Shu | Specifying colors based on group value is a quite popular practice in visualizing data, but it is not so easy to do, especially when there are multiple group values. This paper explores three different methods to dynamically assign colors to plots based on their group values. They are combining EVAL and IFN functions in the plot statements, bringing DISCRETEATTRMAP block into the plot statements, and utilizing the macro from the SAS sample 40255 |
ifn, graph template language | source | 2 |
| 386 | Time Series Analysis: U.S. Military Casualties in the Pacific Theater during World War II | Rachael Becker | This paper aims to show how statistical analy sis can be used in the field of History. The primary focus of this paper is show how SAS can be utilized to obtain a Time Series Analysis of data regarding World War II. The hope of this analysis is to test whe ther Truman's justification for the use of atomic weapons was valid. Truman believed that by using the atomic weapons he would be preventing unacceptable levels of U.S. casualties that would be incurred in the course of a conventional invasion of the Japanese home islands | time series analysis, confidence interval | source presentation |
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| 387 | Tips and Best Practices for Configuring Integrated Windows Authentication | Mike Roda | Since it makes login transparent and does not send passwords over the wire, Integrated Windows Authentication (IWA) is both extremely convenient for end users and highly secure. However, for administrators, it is not easy to set up and rarely successful on the first attempt, so being able to effectively troubleshoot is imperative. In this paper, we take a step-by-step approach to configuring IWA for the SAS middle tier, explain how and where to get useful debugging output, and share our hard- earned knowledge base of known problems and deciphered error messages | error message, data mining, sas administrator | source | 18 |
| 388 | Tips and Strategies for Mixed Modeling with SAS/STAT Procedures | Phil Gibbs | Inherently, mixed modeling with SAS/STAT procedures (such as GLIMMIX, MIXED, and NLMIXED) is computationally intensive. Therefore, considerable memory and CPU time can be required. The default algorithms in these procedures might fail to converge for some data sets and models. This encore presentation of a paper from SAS Global Forum 2012 provides recommendations for circumventing memory problems and reducing execution times for your mixed-modeling analyses. This paper also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, the discussion focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software |
proc step, proc mixed, proc glimmix, covariance, likelihood function, error message, estimate, covariance structure, sas log, random intercept, numeric variable, standard deviation, repeated measures, degrees of freedom, proc nlmixed, response variable, binomial distribution | source | 21 |
| 389 | Tips and Techniques for User-Defined Packages in SAS DS2 | Chris Brooks | account, set statement | source | 20 | |
| 390 | Tips and Techniques for Using Site-Signed HTTPS with SAS 9.4 | Stuart J. Rogers | Are you going to enable HTTPS for your SAS environment? Looking to improve the security of your SAS deployment? Do you need more details about how to efficiently configure HTTPS? This paper guides you through the configuration of SAS 9.4 with HTTPS for the SAS middle tier. We examine how best to implement site-signed Transport Layer Security (TLS) certificates and explore how far you can take the encryption. This paper presents tips and proven practices that can help you be successful | data cleaning, unique key | source | 20 |
| 391 | To Macro or not to Macro: That Is the Question | Claudine Lougee | Do you need a macro for your program? This paper provides some guidelines, based on user experience, and explores whether it's worth the time to create a macro (for example, a parameter -driven macro or just a simple macro variable). This paper is geared toward new users and experienced users who do not use macros | macro variable, macro put | source | 4 |
| 392 | Transform Data Using Expression Builder in SAS Visual Analytics | Atul Kachare | Data transformations serve many functions in data analysis, including improving normality of distribution and equalizing variance to meet assumptions and improve effective sizes. Traditionally, the first step in the analysis is to preprocess and transform the data to derive different representations for further exploration. But now in this era of big data, it is not always feasible to have transformed data available beforehand. Analysts need to conduct exploratory data analysis and subsequently transform da ta on the fly according to their needs. SAS Visual Analytics has an expression builder component Integrated into Visual Data Builder, Visual Analytics Explorer, and Visual Analytics Designer that helps you transform data on the fly. The expression builder enables you to create expressions that you can use to aggregate columns, perform multiple operations on data, and perform conditional processing | date variable, scatter plot | source | 11 |
| 393 | Transformers: Revenge of the PROCs | Faron Kincheloe | Sometimes data are not arranged the way you need them to be for the purpose of reporting or combining with other data. This presentation examines just such a scenario. We focus on the TRANSPOSE procedure and its ability to transform data to meet our needs. We explore this method as an alternative to using "longhand" code involving arrays and OUTPUT statements in a SAS DATA STEP |
proc transpose, proc step, variable list, sample, by group processing, character variable, pdv, array, macro variable, keep statement, numeric variable, output, proc sort, if-then-else, sysdate9, order by | source | 11 |
| 394 | Transforming Data to Information in Education: Stop with the Point-and-Click!!! | Sean W. Mulvenon | Educational systems at the district, state, and national levels all report possessing amazing student-level longitudinal data systems (LDS). Are the LDS systems improving educational outcomes for students? Are they guiding development of effective instructional practices? Are the standardized exams measuring student knowledge relative to the learning expectations? Many questions exist about the effective use of the LDS system and educational data, but data architecture and analytics (including the products developed by SAS are not designed to answer any of these questions. However, the ability to develop more effective educational interfaces, improve use of data at the classroom level, and improve student outcomes might only be available through use of SAS. The purpose of this session is to demonstrate an integrated use of SAS tools to guide the transformation of data to analytics that improve educational outcomes for all students | regression analysis, multiple regression, outlier | source | 9 |
| 395 | Treatment Adaptive Biased Coin Randomization | Gary A. Foster, Lehana Thabane |
Presentation | source | 5 | |
| 396 | UCF SAS Visual Analytics: Implementation, Usage, and Performance | Scott A. Milbuta, Ulf Borjesson, Carlos Piemonti |
At the University of Central Florida (UCF) we recently invested in SAS Visual Analytics, along with the updated SAS Business Intelligence platform (from 9.2 to 9.4), a project that took over a year to be completed. This project was undertaken to give our users the best and most updated tools available. This paper introduces the SAS Visual Analytics environment at UCF and includes projects created using this product. It answers why we selected SAS Visual Analytics for development over other SAS applications. It explains the technical environment for our non-distributed SAS Visual Analytics: RAM, servers, benchmarking, sizing, and scaling. It discusses why we chose the non-distributed mode versus distributed mode. Challenges in the design, implementation, usage, and performance are also presented, including the reasons why Hadoop was not adopted | sas visual analytics, data warehouse | source | 8 |
| 397 | Uncommon Techniques for Common Variables | Christopher J. Bost | If a variable occurs in more than one data set being merged, the last value (from the variable in the right - most data set listed on the MERGE stat ement) is kept. It is critical, therefore, to know the name and sources of any common variables. This paper reviews simple techniques to note overwriting in the log, to summarize the names and sources of all variables, and to identify common variables befo re merging files | proc step, proc freq, proc sql, proc report, proc tabulate, work library, proc contents, where statement, dataset, format statement, merge statement, proc format, proc print, create table, group by, order by | source | 10 |
| 398 | Universal File Flattener | David A. Vandenbroucke | This session describes the construction of a program that converts any set of relational tables into a single flat file, using Base SAS in SAS 9.3. The program gets the information it needs from the data tables themselves, with a minimum of user configuration. It automatically detects one -to-many relationships and creates sets of replicate variables in the flat file. The program illustrates the use of macro programming and the SQL, DATASETS, and TRANSPOSE procedures, among others. The output is sent to a Microsoft EXCEL spreadsheet using the Output Delivery System (ODS) |
macro variable, proc step, flat file, dataset, proc transpose, proc sql, character variable, label statement, work library, numeric variable, excel, merge statement, relational database, macro definition, sample, options statement, output delivery system, %if-%then-%else | source zip |
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| 399 | Unleashing High-Performance Risk Data with the Hadoop Custom File Reader | Mike Whitcher, Stacey M. Christian, Phil Hanna, Don McAlister |
SAS High-Performance Risk distributes financial risk data and big data portfolios with complex analyses across a networked Hadoop Distributed File System (HDFS) grid to support rapid in -memory queries for hundreds of simultaneous users. This data is extremely complex and must be stored in a proprietary format to guarantee data affinity for rapid access. However, customers still desire the ability to view and process this data directly. This paper demonstrates how to use the SAS High-Performance Risk Custom File Reader to directly access risk data in Hadoop MapReduce jobs, using the DS2 procedure, and the LASR procedure |
source zip |
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| 400 | Updating SAS/IntrNet Code Using SAS Visual Analytics, a PHP Interface, and SAS Stored Processes: Changes, Challenges, and Checklists | Laura Simpson | UNC General Administration's Division of Institutional Research (UNCGA IR) collects student-level data on enrollment, graduation, courses, grades, financial aid, facilities, and personnel from the 16 public universities. The university system is transitioning from data collection and processing of flat files submitted via SAS/INTRNET to an Oracle Data Mart that retrieves the data from the campuses' source systems. After collecting the data, either via flat files or data mart, SAS data sets are created and used for analysis and reporting for public and private universities, policy makers, elected officials, and the general public. After a complete turnover in programming staff, UNCGA IR decided to make some major upgrades and improvements to a server and system that were quickly becoming unsupported and outdated |
data mart, flat file, sas consultant, sas enterprise guide, sas administrator, dataset, business intelligence, bar chart, data cleaning | source | 15 |
| 401 | Upgrade from ARIMA to ARIMAX to Improve Forecasting Accuracy of Nonlinear Time-Series: Create Your Own Exogenous Variables Using Wavelet Analysis | Woranat Wongdhamma | This paper proposes a technique to implement wavelet analysis (WA) for improving a forecasting accuracy of the autoregressive integrated moving average model (ARIMA) in nonlinear time -series. With the assumption of the linear correlation, and conventional seasonality adjustment methods used in ARIMA (that is, differencing, X11, and X12), the model might fail to capture any nonlinear pattern. Rather than directly model such a signal, we decompose it to less complex components such as trend, seasonality, process variations, and noises, using WA. Then, we use them as exogenous variables in the autoregressive integrated moving average with explanatory variable model (ARIMAX). We describe a background of WA. Then, the code and a detailed explanation of WA based o n multi -resolution analysis (MRA) in SAS/IML software are demonstrated. The idea and mathematical basis of ARIMA and ARIMAX are also given. Next, we demonstrate our technique in forecasting applications using SAS Forecast Studio |
sample, proc iml, proc step, train-test split, proc freq, regression analysis, predictor variable, arima, response variable, adjusted r-squared, moving average, multiple regression, correlation | source | 16 |
| 402 | Use Capture-Recapture Method To Estimate Prevalence Of Disease In SAS Lin Yan1,2; Lisa M Lix1,2 | Lin Yan, Lisa M. Lix |
Presentation | proc step, logistic regression | source | 3 |
| 403 | Using a SAS Hash Object to Speed and Simplify the Survey Cell Collapsing Process | Ahmed Al-Attar | This paper introduces an extremely fast and simple implementation of the survey cel l collapsing process. Prior implementations had used either several SQL queries or numerous DATA STEP arrays, with multiple data reads. This new approach uses a single hash object with a maximum of two data reads. The hash object provides an efficient and convenient mechanism for quick data storage and retrieval (sub-second total run time) |
hash table, cpu time | source zip |
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| 404 | Using a Single SAS Radar Graphic to Describe Multiple Binary Variables within a Population | Kevin Sundquist, Jacob E Julian, Faith Parsons |
In health care and epidemiological research, there is a growing need for basic graphical output that is clear, easy to interpret, and easy to create. SAS 9.3 has a very clear and customizable graphic called a Radar Graph, yet it can only display the unique responses of one variable and would not be useful for multiple binary variables. In this paper we describe a way to display multiple binary variables for a single population on a single radar graph. Then we convert our method into a macro with as few parameters as possible to make this procedure available for everyday users. Figure 1. Sample radar graph | binary variable, proc step | source zip |
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| 405 | Using Administrative Databases for Research: Propensity Score Methods to Adjust for Bias | Ruth Croxford | Health care and other programs collect large amounts of information in order to administer the program. A health insurance plan, for example, probably records every physician service, hospital and emergency department visit, and prescription medication-information that is collected in order to make payments to the various providers (hospitals, physicians, pharmacists). Although the data are collected for administrative purposes, these databases can also be used to address research questions, including questions that are unethical or too expensive to answer using randomized experiments. However, when subjects are not randomly assigned to treatment groups, we worry about assignment bias -the possibility that the people in one treatment group were healthier, smarter, more compliant, etc., than those in the other group, biasing the comparison of the two treatments | propensity score, sample, logistic regression, regression analysis, logistic distribution, final analysis, health insurance, sample size, treatment effect | source | 10 |
| 406 | Using Analytics to Devise Marketing Strategies for setting a new Business | Anirban Chakraborty | Presentation | source | 5 | |
| 407 | Using Animation to Make Statistical Graphics Come to Life | Jesse M. Pratt | The Statistical Graphics (SG) procedures and the Graph Template Language (GTL) are capable of generating powerful individual data displays. What if one wanted to visualize how a distribution changes with different parameters, how data evolve over time, or to view multiple aspects of a three dimensional plot? By utilizing macros to generate each a graph for each frame, combined with the ODS PRINTER destination, it is possible to create .gif files to create effective animated data displays. This paper outlines the syntax and strategy necessary to generate these displays, as well as provides a handful of examp les. Intermediate knowledge of PROC SGPLOT, PROC TEMPLATE, and the SAS MACRO language is assumed |
normal distribution, proc sgplot, graph template language, proc step, sample, beta distribution, missing value, response variable | source | 10 |
| 408 | Using GENMOD to Analyze Correlated Data on Military Health System Beneficiaries Receiving Behavioral Health Care in South Carolina Health Care System | Abbas S. Tavakoli, Jordan Brittingham, Nikki R. Wooten |
Many SAS procedures can be used to analyze large datasets with correlated data. This study was a secondary analysis of data obtained from the South Carolina Revenue and Fiscal Affairs Office (RFA), which includes medical claims from all health care systems in South Carolina (SC). We used the SAS procedure, GENMOD, to analyze a large dataset with correlated data from Military Health Care (MHS) system beneficiaries who received inpatient behavioral health care in South Carolina Health Care Systems from 2005 to 2014. Behavioral health (BH) was defined by Major Diagnostic Code (MDC) 19 (mental disorders and diseases) and 20 (alcohol and drug use). MDCs are formed by dividing all possible principal diagnoses from the International Classification Diagnostic (ICD-9) codes into 25 mutually exclusive diagnostic categories. The sample included a total of 6,783 BH visits and 4,827 unique military service members, veterans, and their adult and child dependents who have MHS insurance coverage | correlation, frequency analysis, proc genmod, proc freq, proc means, proc step, sample | source presentation |
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| 409 | Using Metadata Queries To Build Row-Level Audit Reports in SAS Visual Analytics | Brandon Kirk | Sensitive data requires elevated security requirements and the flexibility to apply logic that subsets data based on user privileges. Following the SAS Visual Analytics: Administration Guide gives you the ability to apply row-level permission conditions. After you have set the permissions, you have to prove through audits who has access and row-level security. This paper provides you with the ability to easily apply, validate, report, and audit all tables that have row-level permissions, along with the groups, users, and conditions that will be applied. Take the hours of maintenance and lack of visibility out of row -level secure data, and build confidence in the data and analytics provided to the enterprise | access control, where statement | source zip |
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| 410 | Using Parametric and Nonparametric Tests to Assess the Decision of the NBA's 2014-2015 MVP Award | Sherrie Rodriguez | Presentation | source presentation |
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| 411 | Using Predictive Analysis to Optimize Pharmaceutical Marketing | Xue Yao | Most businesses have benefited from utilizing advanced analytics for marketing and other decision making. However, to apply analytical techniques for pharmaceutical marketing is challenging and emerging as it is critical to ensure the analysis makes sense from the medical side. Whether the drug for a specific disease finally consumed is directly and indirectly influenced by many factors including the disease origins, healthcare system policy, physician's clinical decisions and the patient's perceptions and behaviors. The key of pharmaceutical marketing is to identify the targeted populations for specific diseases and to focus on those populations. As the healthcare environment consistently changes, the predictive models are important to predict the change of the targeted population over time based on the patient journey and epidemiology | proc step, time series analysis | source | 6 |
| 412 | Using PROC TABULATE and LAG(n) Function for Rates of Change | Khoi To | For SAS users, PROC TABULATE and PROC REPORT (and its compute blocks) are probably among the most common procedures for calculating and displaying data. It is, however, pretty difficult to calculate and display changes from one column to another using data from other rows with just these two procedures. Compute blocks in PROC REPORT can calculate additional columns, but it would be challenging to pick up values from other rows as inputs. This presentation shows how PROC TABULATE can work with the lag(n) function to calculate rates of change from one period of time to another. This offers the flexibility of feeding into calculations the data retrieved from other rows of the report. PROC REPORT is then used to produce the desired output. The same approach can also be used in a variety of scenarios to produce customized reports |
proc step, proc tabulate, proc report | source zip |
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| 413 | Using Propensity Score Analyses to Adjust for Selection Bias: A Study of Adolescent Mental Illness and Substance Use | Deanna Naomi Schreiber-Gregory | An important strength of observational studies is the ability to estimate a key behavior or treatment's effect on a specific health outcome. This is a crucial strength as most health outcomes research studies are unable to use experimental designs due to ethical and other constraints. Keeping this in mind, one drawback of observational studies (that experimental studies naturally control for) is that they lack the ability to randomize their participants into treatment groups. This can result in the unwanted inclusion of a selection bias. One way to adjust for a selection bias is through the utilization of a propensity score analysis. In this study we provide an example of how to utilize these types of analyses. Our concern is whether recent substance abuse has an effect on an adolescent's identification of suicidal thoughts | propensity score, regression analysis, proc step, logistic distribution, response variable, predictor variable, treatment effect, logistic regression, proc freq, proc logistic, proc glm, proc genmod, proc rank | source | 7 |
| 414 | Using SAS Arrays to Calculate Bouts of Moderate to Vigorous Physical Activity from Minute-by-Minute Fitbit Data | Faith Parsons, Keith M Diaz, Jacob E Julian |
The increasing popularity and affordability of wearable fitness tracking devices, together with their ability to provide granular physical activity data down to the minute-level, has enabled researchers to conduct advanced studies on the effects of physical activity on health and disease. This technology also provides statistical programmers the challenge of processing these data and translating them into analyzable measures. One such measure is the number of time-specific bouts of moderate to vigorous physical activity (MVPA), (akin to 'exercise') which is needed to determine if the participant meets current physical activity guidelines (e.g., 150 min. of MVPA/week performed in bouts of at least 10 min.). In this paper, we illustrate how we used SAS arrays to calculate the number of 10-min. bouts of MVPA per day and provide a working code that can be ran on a sample file | binary variable, macro variable, raw data, array | source zip |
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| 415 | Using SAS Comments to Run SAS Code in Parallel | Jingyu She, Tomislav Kajinic |
Our daily work in SAS involves manipulation of many independent data sets, and a lot of time can be saved if independent data sets can be manipulated simultaneously. This paper presents our interface RunParallel, which opens multiple SAS sessions and controls which SAS procedures and DATA steps to run on which sessions by parsing comments such as /EP.SINGLE/ and /EP.END/. The user can easily parallelize any code by simply wrapping procedure steps and DATA steps in such comments and executing in RunParallel. The original structure of the SAS code is preserved so that it can be developed and run in serial regardless of the RunParallel comments. When applied in SAS programs that access data from many different data sources and heavy computations, RunParallel can give major performance boosts | sas base, proc step, data step, proc sort | source | 17 |
| 416 | Using SAS for a Data Visualization Study of the Distribution of Same-Sex Couples by State as Marriage Laws Change | Bula Ghose, Susan Queen, Joan Turek |
Presentation: Marriage laws have changed in 38 states, including the District of Columbia, permitting same-sex couples to marry since 2004. However, since 1996, 13 states have banned same-sex marriages, some of which are under legal challenge. A U.S. animated map, made using the SAS GMAP procedure in SAS 9.4, shows the changes in the acceptance, rejection, or undecided legal status of this issue, year-by-year. In this study, we also present other SAS data visualization techniques to show how concentrations (percentages) of married same-sex couples and their relationships have evolved over time, thereby instigating changes in marriage laws. Using a SAS GRAPH display, we attempt to show how both the total number of same-sex-couple households and the percent that are married have changed on a state-by-state basis since 2005. These changes are also compared to the year in which marriage laws permitting same-sex marriages were enacted, and are followed over time since that year |
source zip presentation |
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| 417 | Using SAS Geocoding as a Form of Field Interview Verification | Chris Carson, Lilia Filippenko, Mai Nguyen |
The Add Health Parent Study is using a new and innovative method to augment our other interview verification strategies. Typical verification strategies include calling respondents to ask questions about their interview, recording pieces of interaction (CARI - Computer Aided Recorded Interview), and analyzing timing data to see that each interview was within a reasonable length. Geocoding adds another tool to the toolbox for verifications. By applying street-level geocoding to the address where an interview is reported to be conducted and comparing that to a captured latitude/longitude reading from a GPS tracking device, we are able to compute the distance between two points. If that distance is very small and time stamps are close to each other, then the evidence points to the field interviewer being present at the respondent's address during the interview. For our project, the street-level geocoding to an address is done using SAS PROC GEOCODE |
proc step, data cleaning, sample, proc sql | source | 12 |
| 418 | Using SAS Programming, SAS Enterprise Guide, and SAS Visual Analytics to Generate and Disseminate National Postgraduate Programs Data in a Mobile Computing Environment | Leonardo de Lima Aguirre, Sergio da Costa Cortes, Marcus Vinicius de Olivera Palheta |
The Coordination for the Improvement of Higher Education Personnel (CAPES) is a foundation within the Ministry of Education in Brazil whose central purpose is to coordinate efforts to promote high standards for postgraduate programs inside the country. Structured in a SAS data warehouse, vast amounts of information about the National Postgraduate System (SNPG) is collected and analyzed daily. This data must be accessed by different operational and managerial profiles, on desktops and mobile devices (in this case, using SAS Mobile BI). Therefore, accurate and fresh data must be maintained so that is possible to calculate statistics and indicators about programs, courses, teachers, students, and intellectual productions. By using SAS programming within SAS Enterprise Guide all statistical calculations are performed and the results become available for exploration and presentation in SAS Visual Analytics | sas enterprise guide, excel | source zip |
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| 419 | Using SAS Simulation Studio to Test and Validate SAS/OR Optimization Models | Edward P. Hughes, Emily Lada, Leo Lopes, Imre Polik |
In many discrete-event simulation projects, the chief goal is to investigate the performance of a system. You can use output data to better understand the operation of the real or planned system and to conduct various "what-if" analyses. But you can also use simulation for validation-specifically, to validate a solution found by an optimization model. In optimization modeling, you almost always need to make some simplifying assumptions about the details of the system you are modeling. These assumptions are especially important when the system includes random variation-for example, in the arrivals of individuals, their distinguishing characteristics, or the time needed to complete certain tasks. A common approach holds each random element at some nominal value (such as the mean of its observed values) and proceeds with the optimizati on. You can do better | odds ratio, proc step, sample | source zip |
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| 420 | Using SAS Studio Tasks to Plot with ODS Graphics | Philip R. Holland | SAS Studio includes Tasks that can be used to generate SAS programs to process SAS data sets. The Graph Tasks generate SAS programs that use ODS Graphics to produce a range of plots and charts. However, SAS Enterprise Guide 7.1 and SAS Add-In for Microsoft Office 7.1 can also make use of these SAS Studio Tasks to generate graphs with ODS Graphics, even though their built-in Tasks use SAS/GRAPH. This paper describes these SAS Studio Graph Tasks |
bar chart, sas studio, proc step, horizontal bar chart, vertical bar chart, proc sgplot, box plot, sas enterprise guide | source | 12 |
| 421 | Using SAS Text Miner for Automatic Categorization of Blog Posts on a Social Networking Site Dedicated to Cyclists | Heramb Joshi, Goutam Chakraborty |
Cycling is one of the fastest growing recreational activity and sport in India. There are many NGOs (Non - government Organizations) supporting this emission free mode of transport and help protect environment from air pollution hazards. There are lot of cyclists groups in metropolitan areas which organize numerous ride events and build social networks. Though India was a bit late for joining the Internet, the social networking sites are getting popular in every Indian states and is expected to grow even faster after announcement of Digital India Project. There are many networking groups and cycling blogs sharing a plethora of information and resources across the globe. However one concern is that these blogs posts are hard to categorize according to their relevance. This makes it difficult to access required information quickly by referring to those blogs | parsing, dataset, sample, data cleaning | source | 11 |
| 422 | Using SAS to Conduct Multivariate Statistical Analysis in Educational Research: Exploratory Factor Analysis and Confirmatory Factor Analysis | Jun Xu, Steven L. Holtzman, Kevin Petway, Lili Yao |
Multivariate statistical analysis plays an increasingly important role as the number of variables being measured increases in educational research. In both cognitive and noncognitive assessments, many instruments that researchers aim to study contain a large amount of variables where each measured variable can be assigned to a specific factor of a bigger construct. The factor of an instrument typically emerges through the use of educational theories or empirical research | factor analysis, correlation, maximum likelihood estimation, proc calis, proc step, likelihood function, proc factor, latent variable, data science, covariance | source | 13 |
| 423 | Using SAS to Implement Simultaneous Linking in Item Response Theory | Lili Yao | In item-response theory (IRT), item parameters estimated from examinee responses characterize performance of items from a test form used in administration of an educational assessment (Hambleton, Swaminathan, & Rogers, 1991, ch. 1). These parameters are specific to the proficiency distribution of the examinees for that administration. When an assessment must be administered at many different times, many different test forms must be prepared. Typically the proficiency distributions of examinees differ for tests administered at different times, so that item parameters for different test forms are not comparable. Achievement of comparability relies on items common to more than one test form. These items permit linkage of the parameters for all test forms so that these parameters apply to a common proficiency distribution of test takers. Simultaneous linking (Haberman, 2009) uses regression analysis to perform the required linking | proc genmod, latent variable, regression analysis, proc glm, standard deviation, ods output, macro definition, output delivery system | source | 6 |
| 424 | Using SAS to Integrate the LACE | Readmissions Risk Score | Presentation | source | 4 | |
| 425 | Using SAS Visual Analytics to Develop a Real-Time Business Metrics Command Center for Your Office | Christopher Blake | Seeing business metrics in real time enables a company to understand and respond to ever -changing customer demands. In reality, though, obtaining such metrics in real time is not always easy. However, SAS Australia and New Zealand Technical Support solved that problem by using SA S Visual Analytics to develop a 16-display command center in the Sydney office. Using this center to provide real -time data enables the Sydney office to respond to customer demands across the entire South Asia region. The success of this deployment makes reporting capabilities and data available for Technical Support hubs in Wellington, Mumbai, Kuala Lumpur, and Singapore-covering a total distance of 12,360 kilometers (approximately 7,680 miles) | big data, mysql, structured data, excel, sql server, macro variable | source | 19 |
| 426 | Using SASIOTEST to Measure I/O Throughput | Mike Jones | Have you questioned the Read throughput or Write throughput for your Windows system drive? What about the input/output (I/O) throughput of a non-system drive? One solution is to use SASIOTEST.EXE to measure the Read or Write throughput for any drive connected to your system. Since SAS 9.2, SASIOTEST.EXE has been included with each release of SAS for Windows. This paper explains the options supported by SASIOTEST and the various ways to use SASIOTEST. It also describes how the I/O relates to SAS I/O on Windows | dataset, elapsed time, numeric variable, proc step, work library | source | 15 |
| 427 | Using the DOCUMENT Procedure to Expand the Output Flexibility of the Output Delivery System with Very Little Programming Effort | Roger D. Muller | The DOCUMENT procedure is a little known procedure that can save you vast amounts of time and effort when managing the output of your SAS programming efforts. This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (ODS). Have you ever wished you didn't have to modify and rerun the report-generating program every time there was some tweak in the desired report? PROC DOCUMENT enables you to store one version of the report as an ODS Document Object and then call it out in many different output forms, such as PDF, HTML, listing, RTF, and so on, without rerunning the code. Have you ever wished you could extract those pages of the output that apply to certain BY variables such as State, StudentName, or CarModel? With PROC DOCUMENT, you have where capabilities to extract these |
proc step, output delivery system | source | 8 |
| 428 | Using the Kaplan-Meier Product-Limit Estimator to Adjust NFL Yardage Averages | Keith G. Curtis | Average yards per reception, as well as number of touchdowns, are commonly used to rank National Football League (NFL) players. However, scoring touchdowns lowers the player's average since it stops the play and therefore prevents the player from gaining more yardage. If yardages are tabulated in a life table, then yardage from a touchdown play is denoted as a right-censored observation. This paper discusses the application of the SAS/STAT Kaplan-Meier product- limit estimator to adjust these averages. Using 15 seasons of NFL receiving data, the relationship between touchdown rates and average yards per reception is compared, before and after adjustment. The modification of adjustments when a player incurred a 2-point safety during the season is also discussed |
censoring, regression analysis, bubble plot, proc step, kaplan-meier estimator, proc freq, sample | source | 20 |
| 429 | Using the OPTMODEL Procedure in SAS/OR to Find the k Best Solutions | Robert C. Pratt | Because optimization models often do not capture some important real-world complications, a collection of optimal or near-optimal solutions can be useful for decision makers. This paper uses various techniques for finding the k best solutions to the linear assignment problem in order to illustrate several features recently added to the OPTMODEL procedure in SAS/OR software. These features include the network solver, the constraint programming solver (which can produce multiple solutions), and the COFOR statement (which allows parallel execution of independent solver calls) |
proc step, odds ratio | source zip |
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| 430 | Using the REPORT Procedure to Export a Big Data Set to an External XML File | Guihong Chen | A number of SAS tools can be used to report data such as the PRINT, MEANS, TABULATE, and REPORT procedures. The REPORT procedure is a single tool that can cover features of the other SAS tools. Not only can it produce detail reports like PROC PRINT, but it can summarize and calculate data as in the MEANS and TABULATE procedures. Unfortunately, despite its power, PROC REPORT seems to be used less often than the other tools, possibly due to its seemingly complex coding. This paper uses PROC REPORT under the ODS environment to export a big data set into a colorful and customized xml file so that a non-SAS user can read the data easily in the xml file for useful information. DEFINE, COMPUTE, and COLUMN statements are used in the paper along with several options to present your data in a more colorful way. It shows how to control the format of the selected columns and rows. The headings of columns can be more meaningful and pretty |
proc step, proc report, excel, character variable | source zip presentation |
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| 431 | Using the SAS Deployment Backup and Recovery Tool in the Third Maintenance Release of SAS 9.4 | Bhaskar Kulkarni | The SAS Deployment Backup and Recovery Tool in the third maintenance release of SAS 9.4 helps SAS administrators to collectively take backups of important data artifacts in SAS deployments, which include SAS Metadata Server, SAS Content Server, SAS Web Infrastructure Platform Data Server, and physical data in the SAS configuration directory. The tool supports all types of deployments, from single-tier to multi-tier clustered heterogeneous host deployments. The new configuration options in the tool give administrators more control over the data that is being backed up, fr om the SAS tiers to the individual directory level. The new options allow administrators to filter out old log directories and to choose the databases to back up. This paper talks about not only how to use the configuration options but also how to mediate the effects that the SAS deployment configuration changes have on the backups and how to optimize the backups in terms of size and time | sample, sas administrator | source | 7 |
| 432 | Using the SAS Hash Object with Duplicate Key Entries | Paul M. Dorfman | By default, the SAS hash object permits only entries whose keys, defined in its key portion, are unique. While in certain programming applications this is a rather utile feature, there are also others for which being able to insert and manipulate entries with duplicate keys is imperative. Such an ability, facilitated in SAS since SAS 9.2, was a welcome development: It vastly expanded the functionality of the hash object and eliminated the necessity to work around the distinct-key limitation using custom code. However, nothing comes without a price, and the ability of the hash object to store duplicate key entries is no exception. In particular, additional hash object methods had to be-and were- developed to handle specific entries sharing the same key. The extra price is that using these methods is surely not quite as straightforward as the simple corresponding operations on distinct-key tables, and the documentation alone is a rather poor help for making them work in practice | hash table, do loop, sas log, sysrc, unique key, sample | source zip |
16 |
| 433 | Using the SURVEYSELECT Procedure to Draw Stratified Cluster Samples with Unequally Sized Clusters | Chuanjie Liao, Brian Patterson |
PROC SURVEYSELECT is a useful procedure for sample selection for a wide variety of applications. This paper presents the application of PROC SURVEYSELECT to a complex scenario involving a state - wide educational testing program, namely drawing inter-dependent stratified cluster samples of schools for the field-testing of test questions, which we call "items". These stand-alone field tests are given to only small portions of the testing population and as such, a stratified procedure was used to ensure representativeness of the field-test samples. As these items' field test statistics are evaluated for use in future operational tests, an efficient procedure is needed to sample schools, while satisfying pre -defined sampling criteria and targets. This paper provides an adaptive sampling application and then generalizes the methodology as much as possible for potential use in more industries |
sample, proc surveyselect, proc step, sample size, sample weight, stratified sampling, macro variable, cluster sampling, proc freq | source presentation |
11 |
| 434 | Virtual Accessing of a SAS Data Set Using OPEN, FETCH, and CLOSE Functions with %SYSFUNC and %DO Loops | Amarnath Vijayarangan | One of the truths about SAS is that there are, at a minimum three approaches to achieve any intended task and each approaches has its own pros and cons. Identifying and using of efficient SAS programming techniques are recommended and efficient programming becomes mandatory when th e larger data sets are accessed. This paper describes the efficiency of virtual access and various situations to virtual access of the data sets using OPEN, FETCH and CLOSE functions with %SYSFUNC and %DO | macro variable, variable list, length statement, macro loop, macro function, correlation, feature engineering, regression analysis | source presentation |
6 |
| 435 | Visualizing Eye-Tracking Data with SAS: Creating Heat Maps on Images | Matthew Duchnowski | Few data visualizations are as striking or as useful as heat maps. Fortunately, there are many applications for them. A heat map that displays eye-tracking data is a particularly potent example: the intensity of a viewer's gaze is quantified and superimposed over the image being viewed. The resulting display is often stunning and informative. Using Base SAS, the author shows how to prepare, smooth and transform eye-tracking data and ultimately render it on top of the corresponding image. By customizing a graphical template and using specific Graphical Template Language (GTL) options, a heat map can be drawn precisely so that the user maintains pixel-level control. In this paper, eye-tracking data will be used, but the techniques provided are easily adapted to data from other fields such as sports, weather and marketing | heatmap, proc step, output delivery system, proc freq | source presentation |
9 |
| 436 | What about When It's Down? An Application for the Enhancement of the SAS Middle-Tier User Experience | Christopher Blake | The SAS Web Application Server goes down and the user is presented with an error message. The error messages in the SAS 9.4 middle tier are the default ones that are shipped with the underlying VMware vFabric Web Server and are seen by many users as too technical and uninformative. This paper describes an application called "Errors" that was developed at the Royal Bank of Scotland that has been implemented across its 9.4 estate to provide a much better user experience for when things go wrong. In addition, r egardless of communications, users always try to access an application if it is available. This paper goes into detail about a feature of the Errors application that RBS uses to prevent this. This feature is used to control access to the web applications d uring scheduled outage windows and it provides capability for IP and location -based access as well as others. This paper also documents features and capabilities that RBS would like to introduce to the application | error message, bootstrap | source | 11 |
| 437 | What to Expect When You Need to Make a Data Delivery... Helpful Tips and Techniques | Tom McCall, Louise S. Hadden |
Making a data delivery to a client is a complicated endeavor. There are many aspects that must be carefully considered and planned for: de-identification, public use versus restricted access, documentation, ancillary files such as programs, formats, and so on, and methods of data transfer, among others. This paper provides a blueprint for planning and executing your data delivery | data wrangling, dataset, proc step, excel, label statement | source presentation |
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| 438 | What's New in SAS Data Management | Nancy A. Rausch | The latest releases of SAS Data Integration Studio, SAS Data Management Studio and SAS Data Integration Server, SAS Data Governance, and SAS/ACCESS software provide a comprehensive and integrated set of capabilities for collecting, transforming, and managing your data. The latest features in the product suite include capabilities for working with data from a wide variety of environments and types including Hadoop, cloud, RDBMS, files, unstructured data, streaming, and others, and the ability to perform ETL and ELT transformations in diverse run-time environments including SAS, database systems, Hadoop, Spark, SAS Analytics, cloud, and data virtualization environments. There are also new capabilities for lineage, impact analysis, clustering, and other data governance features for enhancements to master data and support metadata management |
big data, data governance, proc sql | source | 18 |
| 439 | What's New in SAS Federation Server 4.2 | Tatyana Petrova | The SAS Federation Server is a scalable, threaded, multi-user data access server that provides seamless, integrated data from various data sources. When your data becomes too large to move or copy and too sensitive to allow direct access, the powerful set of data virtualization capabilities allow s you to effectively and efficiently manage and manipulate data from many sources, without moving or copying the data. This agile data integration framework will allow business users the ability to connect to more data, reduce risks, respond faster, and make better business decisions. For technical users, the framework provides central data control and security, reduces complexity, optimizes commodity hardware, promotes rapid prototyping, and increases staff productivity. This paper provides an overview of the latest features of the product and includes examples for leveraging product capabilities | data warehouse, sas base, access control, data masking, data cleaning | source | 13 |
| 440 | What's the Difference? | David A. Dickey | Each night on the news we hear the level of the Dow Jones Industrial Average along with the "first difference," which is today's price-weighted average minus yesterday's. It is that series of first differences that excites or depresses us each night as it reflects whether stocks made or lost money that day. Furthermore, the differences form the data series that has the most addressable statistical features. In particular, the differences have the stationarity requirement, which justifies standard distributi onal results such as asymptotically normal distributions of parameter estimates. Differencing arises in many practical time series because they seem to have what are called "unit roots," which mathematically indicate the need to take differences. In 1976, Dickey and Fuller developed the first well-known tests to decide whether differencing is needed. These tests are part of the by SAS ARIMA procedure in SAS/ETS in addition to many other time series analysis products |
unit root, regression analysis, time series analysis, null hypothesis, first difference, sample, proc step, proc arima, stationarity, student t distribution, sample size, proc reg, autoregressive model, estimate, moving average, normal distribution, residual, f-test, histogram | source | 17 |
| 441 | When Reliable Programs Fail: Designing for Timely, Efficient, Push-Button Recovery | Troy Martin Hughes | Software quality comprises a combination of both functional and performance requirements that together specify not only what software should accomplish, but also how well it should accomplish it. Recoverability-a common performance objective-represents the timeliness and efficiency with which software or a system can resume functioning following a catastrophic failure. Thus, requirements for high availability software often specify the recovery time objective (RTO), or the maximum amount of time that software may be down following an unplanned failure or a planned outage. While systems demanding high or near perfect availability will require redundant hardware, network, and additional infrastructure, software too must facilitate rapid recovery. And, in environments in which system or hardware redundancy is infeasible, recoverability only can be improved through effective softwa re development practices | data validation rules, macro variable, quality control, sas programmer, exception handling, automatic macro variable, constraint, macro function | source | 14 |
| 442 | When the Answer to Public or Private Is Both: Managing a Hybrid Cloud Environment | Ethan Merrill, Bryan Harkola |
For many organizations, the answer to whether to manage their data and analytics in a public or private cloud is going to be "both." "Both" can be the answer for many different reasons: common sense logic not to replace a system that already works just to incorporate something new; legal or corporate regulations that require some data, but not all data, to remain in place; and even a desire to provide local employees with a traditional data center experience while providing remote or international employees with cloud - based analytics easily managed through software deployed via Amazon Web Services (AWS) | access control, sas administrator | source | 5 |
| 443 | Who's Your Neighbor? A SAS Algorithm for Finding Nearby Zip Codes | Andrew W. Clapson, Annmarie Smith |
Even if you're not a GIS mapping pro, it pays to have some geographic problem-solving techniques in your back pocket. In this paper we'll illustrate a general approach to finding the closest location to any given US zip code, with a specific, user accessible example of how to do it using only Base SAS. We al so suggest a method for implementing the solution in a production environment, as well as demonstrate how parallel processing can be used to cut down on comp uting time if there are hardware constraints | hash table, sample, parallel processing, sas base, lookup, cpu time | source zip |
7 |
| 444 | Working with Big Data in Near Real Time Using SAS Event Stream Processing | Kishore Konudula | Presentation | stream processing, real-time processing | source | 6 |
| 445 | Working with PROC SPP, PROC GMAP and PROC GINSIDE to Produce Nice Maps | Alan Ricardo da Silva | This paper aims to show how to create maps from the output of new PROC SPP using the same layer of the data. This cut can be done by PROC GINSIDE and the final plot can be drawn by PROC GMAP. The result is a nice map and nicer than the plot produced by PROC SPP |
proc step, proc gmap, %if-%then-%else | source presentation |
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| 446 | Writing Packages: A New Way to Distribute and Use SAS/IML Programs | Rick Wicklin | SAS/IML 14.1 enables you to author, install, and call packages. A package consists of SAS/IML source code, documentation, data sets, and sample programs. Packages provide a simple way to share SAS/IML functions. An expert who writes a statistical analysis in SAS/IML can create a package and upload it to the SAS/IML File Exchange. A nonexpert can download the package, install it, and immediately start using it. Packages provide a standard and uniform mechanism for sharing programs, which benefits both experts and nonexperts. Packages are very popular with users of other statistical software, such as R. This paper describes how SAS/IML programmers can construct, upload, download, and install packages. They're not wrapped in brown paper or tied up with strings, but they'll soon be a few of your favorite things! |
proc iml, proc step | source | 10 |
| 447 | Writing Reusable Macros | Philip R. Holland | I get annoyed when macros tread all over my SAS environment, macro variables and data sets, so how do you write macros that play nicely and then clean up afterwards? This paper describes techniques for writing macros that do just that | macro variable, macro call, proc sql, macro definition | source | 3 |
| 448 | You Can Bet on It: Missing Observations Are Preserved with the PRELOADFMT and COMPLETETYPES Options | Christopher J. Boniface, Janet Wysocki |
Do you w rite reports that sometimes have missing categories across all class variables? Most times the customer or sponsor w ants to see those missing categories in the report. Some programmers w ill w rite all sorts of additional DATA STEP code in order t o show the zeroes for the missing row s or columns. Did you ever ponder that there must be an easier w ay to accomplish this? Well, PROC MEANS and PROC TABULATE in conjunction w ith PROC FORMAT can handle this situation w ith a couple of pow erful options. With PROC TABULATE, w e can use the PRELOA DFMT and PRINTMISS options in conjunction w ith a user-defined format w ith PROC FORMAT to accomplish this task. With PROC SUMMARY, w e can use the COMPLETETY PES option to get all the row s w ith zeroes. The Census Bur eau produces special tabulations for many sponsors. Often the sponsor w ill w ant a report w ith various counts across various categorical variables |
proc step, proc tabulate, proc means, proc format, class variable, missing value, nominal variable, user-defined format, format statement | source | 7 |
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