SGF 2015 Papers - Index

413 papers. Source: SAS Global Forum conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	"Before and After" Models in Observational Research Using Random Slopes and Intercepts	David J. Pasta	In observational data analyses, it is often useful to use patients as their own controls by comparing their outcomes "before" and "after" some signal event, such as the initiation of a new therapy. It may be useful to have a control group that does not have the event but instead is evaluated before and after some arbitrary point in time, such as their birthday. In this context, the change over time is a continuous outcome that can be modeled as a (possibly discontinuous) line, with the same or different slope before and after the event. Mixed models can be used to estimate random slopes and intercepts and compare patients between groups. A specific example published in a peer-reviewed journal is presented	regression analysis, proc mixed, correlation	source	12
2	"Out Here" Forecasting: A Retail Case Study	Chris Houck	Faced with diminishing forecast returns from the forecast engine within the existing replenishment application, Tractor Supply Company (TSC) engaged SAS Institute to deliver a fully integrated forecasting solution that promised a significant improvement of chain-wide forecast accuracy. The end- to-end forecast implementation including problems faced, solutions delivered, and results realized will be explored	time series analysis, regression analysis, residual, cv, demand forecasting, proc genmod	source	9
3	"Puck Pricing": Dynamic Hockey Ticket Price Optimization	Christopher Jones, Sabah Sadiq, Jing Zhao	Dynamic pricing is a real-time strategy where corporations adjust prices "on the fly" in response to changing market demand. The hospitality industry has widely adopted this strategy to maximize expected revenue by raising prices in the summer or weekends when demand for hotel rooms is at a premium. In recent years, the sports industry has started to catch on to this trend. As the strategies revolutionized baseball ticketing, more hockey teams have started to experiment dynamic pricing. The purpose of this paper is to explore the methodology of applying dynamic pricing to hockey ticketing arena	price elasticity, sample size	source	10
4	A Case Study: Improve Classification of Rare Events with SAS Enterprise Miner	Ruizhe Wang, Novik Lee, Yun Wei	Imbalanced data are frequently seen in fraud detection, direct marketing, disease prediction and many other areas. Rare events are sometimes of our primary interest and to classify them correctly are the challenges many predictive modelers face today. In this paper, we use SAS Enterprise Miner on a marketing data set to demonstrate and compare several approaches commonly used to handle imbalanced data problems in classification models. The approaches are based on cost-sensitive measures and sampling measures. A rather novel technique called SMOTE (Synthetic Minority Over-sampling TEchnique), which has achieved the best result in our comparison, is discussed	sample, roc curve, prior probability, data mining, neural network, network optimization, interval variable, imbalanced data, training set	source zip	12
5	A Configurable SAS Framework for Managing a Reporting System Based on SAS OLAP Cube Studio	Ahmed Al-Attar	This paper illustrates a high-level infrastructure discussion with some explanation of the SAS codes used to implement a configurable batch framework for managing and updating SAS OLAP Cube's data rows and row level permissions. The framework contains collection of reusable parameter driven SAS Base macros, SAS Base custom programs and UNIX/LINUX shell scripts. This collection manages typical steps and processes used for manipulating SAS files and executing SAS statements. The SAS Base macro collections contains a group of Utility Macros that includes Concurrent /Parallel Processing Macros SAS Metadata Repository Macros SPDE Tables Macros Table Lookup Macros Table Manipulation Macros Other Macros And a group of OLAP related Macros, that includes OLAP Utility Macros OLAP Permission Table Processing Macros	macro variable, sas base, macro call, proc step	source	25
6	A Genetic Algorithm for Data Reduction	Lisa Henley	When large amounts of data are available, choosing the variables for inclusion in model building can be problematic. In this analysis, a subset of variables was required from a larger set. This subset was to be used in a later cluster analysis with the aim of extracting dimensions of human flourishing. A genetic algorithm (GA), written in SAS, was used to select the subset of variables from a larger set in terms of their association with the dependent variable life satisfaction. Life satisfaction was selected as a proxy for an as yet undefined quantity, human flourishing. The data were divided into subject areas ( for example health, environment). The GA was applied separately to each subject area to ensure adequate representation from each in the future analysis when defining the human flourishing dimensions	regression analysis, response variable, predictor variable, sample, significance level, proc reg	source zip	7
7	A Macro for Computing the Best Transformation	Nancy Hu	This study is intended to assist Analysts to generate the best of variables using simple arithmetic operators (square root, log, loglog, exp and rcp) and such as monthly amount paid, daily number of received customer service calls, or annual number total sales for a specific product. During a statistical data modeling process, Analysts are often confronted with the task of computing derived variables using the existing available variables. The advantage of this methodology is that the new variables may be more significant than the original ones. This paper gives a new way to compute all the possible variables using a set of math transformation. The codes include many SAS features that are very useful tools for SAS programmers to incorporate in their future codes Such as %SYSFUNC, SQL, `%INCLUDE`, CALL SYMPUT, `%MACRO`, SORT, CONTENTS, MERGE, MACRO `_NULL_`, as well as %DO …%TO … and many more. I demonstrate the syntax and mechanics of the macro using following examples	macro variable, macro definition	source zip	9
8	A Macro to Easily Generate a Calendar Report	Ting Sa	This paper introduces a macro that generates a calendar report in two different formats. The first format displays the entire month in one plot, which is called a month-by-month calendar report. The second format displays the entire month in one row and is called an all-in-one calendar report. To use the macro, you just need to prepare a simple data set that has three columns: one column identifies the ID, one column contains the date, and one column specifies the notes for the dates. On the generated calendar reports, you can include notes and add different styles to certain dates. Also, the macro provides the option for you to decide whether those months in your data set that do not contain data should be shown on the reports	sample, in=	source	8
9	A Mathematical Model for Optimizing Product Mix and Customer Lifetime Value	Atul Thatte, Goutam Chakraborty	Companies that offer subscription based services (such as telecom and fitness clubs) must evaluate the tradeoff between non-contract customers, who yield lower value due to lower lifetime , and customers who commit to a longer term contract in return for lo wer price. The objective, of course, is to maximize the Customer Lifetime Value (CLV). This tradeoff must be evaluated not only at the time of customer acquisition, but throughout the customer's tenure, particularly for fixed -term contract customers whose contract becomes due for renewal. In this paper, we present a mathematical model that maximizes the revenue against this tradeoff with customer lifetime value. We use revenue as a proxy for CLV, to keep things simple. The model can be easily extended to t ake into account cash flow discounting , account for activity based costing, etc. The model is presented in the context of a cohort of new customers, some of whom are non-contract customers and others are on contracts	customer lifetime value, create table, group by, regression analysis, proc sql	source zip	16
10	A Metadata to Physical Table Comparison Tool	David Moors	When promoting metadata in large packages between environments, metadata and the underlying physical data can become out of sync. This can result in metadata items that cannot be opened because SAS clients have thrown an error. It often falls to the SAS administrator to resolve the synchronization issues when they might not have even been responsible for promoting the metadata items in the first place. In this paper, we will discuss a simple program that can be used to compare the table metadata to that of the physical tables, and any anomalies will be noted. METADATA With the introduction of the SAS 9 platform in 2002, SAS introduced the concept of metadata as a way of providing a centralised data layer between all SAS Applications. The official definition of the metadata server taken from the SAS website is as follows: The SAS Metadata Server is a centralized resource for storing, managing, and delivering metadata for SAS applications across the enterprise	proc step, sas enterprise guide, proc contents, proc compare, dataset, macro parameter, macro variable, sas 9, sas data integration studio	source zip	15
11	A Model Family for Hierarchical Data with Combined Normal and Conjugate Random Effects	Geert Molenberghs	Presentation	poisson distribution, binomial distribution, proc mixed, estimate, likelihood function, logistic distribution	source	54
12	A New Method of Using Polytomous Independent Variables with Many Levels for the Binary Outcome of Big Data Analysis	John Gao, Jesse Harriott, Lisa Pimentel	The paper discuss es a new method in logistic regression for polytomous independent variables with many levels in big data. In the proposed method, the first step is to conduct an iterati ve statistical analysis from a SAS Macro program to search for the proper aggregation groups with statistical ly significant differences from all levels in a poly tomous independent variable. Then the mean values at the new aggregation groups are used for a new independent variable, which is a numerical, can be regarded as a continuous and is going to replace the original variable in the following statistical analysis	predictor variable, data science, big data, logistic distribution, proc logistic, macro definition, proc step	source	7
13	A Practical Approach to Managing a Multi-Tenant SAS Intelligence Platform Deployment	Jim Fenton, Robert Ladd	Modernizing SAS assets within an enterprise is key to reducing costs and improving productivity. Modernization implies consolidating multiple SAS environments into a single shared enterprise SAS deployment. While the benefits of modernization are clear, the management of a single-enterprise deployment is sometimes a struggle between business units who once had autonomy and IT that is now responsible for managing this shared infrastructure. The centralized management and control of a SAS deployment is based on SAS metadata. This paper provides a practical approach to the shared management of a centralized SAS deployment using SAS Management Console. It takes into consideration the day-to-day needs of the business and IT requirements including centralized security, monitoring, and management	sas management console, sas administrator, access control, sas grid	source	11
14	A Practical Guide to SAS Extended Attributes	Chris Brooks	All SAS data sets and variables have standard attributes. These include items such as creation date, engine, compression and sort information for data sets, and format and length information for variables. However, for the first time in SAS 9.4, the developer can add their own customized attributes to both data sets and variables. This paper shows how these extended attributes can be created, modif ied, and maintained. It suggests the sort of items that might be candidates for use as extended attributes and explains in what circumstances they can be used. It also provides a worked example of how they can be used to inform and aid the SAS programmer in creating SAS applications	proc step, proc contents, set statement, sas programmer, proc sql	source	9
15	A Quick View of SAS Views	Elizabeth Axelrod	Looking for a handy technique to have in your toolkit? Consider SAS Views, especially if you work with large data sets. After a brief introduction to Views, I'll show you several cool ways to use them that will streamline your code and save workspace	data step, cpu time, proc step, view, proc sort, dataset, proc freq	source	7
16	A SAS Macro to Calculate the PDC Adjustment of Inpatient Stays	Anping Chang	The Center for Medicare &Medicaid Services (CMS) uses the Proportion of Days Covered (PDC) to measure medication adherence among their beneficiaries. There is also some PDC-related research based on Medicare Part D Event (PDE) Data. However, "Under Medicare rules, beneficiaries who re ceive care at an Inpatient (IP) may receive Medicare covered medications directly from the IP, rather than by filling prescriptions through their Part D contractors; thus, their medication fills during an IP stay would not be included in the PDE claims used to calculate the Patient Safety adherence measures."(Medicare 2014 Part C&D star rating technical notes). Therefore, the previous PDC calculation method underestimated the true values. Beginning with the 2013 Star rating, PDC calculations are adjusted for IP stays, by censoring IP stays during the measurement period	hash table, macro definition	source zip	10
17	A SAS Macro to Compare Predictive Values of Diagnostic Tests	Lovedeep Gondara	Medical tests are used for various purposes including diagnosis, prognosis, risk assessment and screening. Statistical methodology is used often to evaluate such types of tests, most frequent measures used for binary data being sensitivity, specificity, positive and negative predictive values. An important goal in diagnostic medicine research is to estimate and compare the accuracies of such tests. In this paper I give a gentle introduction to measures of diagnostic test accuracy and introduce a SAS macro to calculate generalized score statistic and weighted generalized score statistic for comparison of pre dictive values using formula's generalized and proposed by Andrzej S. Kosinski	negative predictive value, diagnostic accuracy	source	11
18	A Set of SAS Macros for Generating Survival Analysis Reports for Lifetime Data with or without Competing Risks	Zhen-Huan Hu	The paper introduces users to how they can use a set of SAS macros, %LIFETEST and %LIFETESTEXPORT, to generate survival analysis reports for data with or without competing risks. The macros provide a wrapper of the `LIFETEST` procedure and an enhanced version of the AUTOCALL macro %CIF to give users an easy-to-use interface to report both survival estimates and cumulative incidence estimates in a unified way. The macros also provide a number of parameters to enable users to flexibly adjust how the final reports should look without the need to manually input or format the final reports	overall survival, survival analysis, competing risks, group by, kaplan-meier estimator, class variable, autocall macro, confidence interval	source zip	6
19	A Tool That Uses the SAS PRX Functions to Fix Delimited Text Files	Paul Genovesi	Delimited text files are often plagued by appended and/or truncated records. Writing customized SAS code to import such a text file and break out into fields can be challenging. If only there was a way to "fix" the file before importing it. Enter the file_fixing_tool, a SAS Enterprise Guide project that uses the SAS PRX functions to import, fix, and export a delimited text file. This fixed file can then be easily imported and broken out into fields	txt, macro variable, delimited file, dataset, sas enterprise guide, macro definition	source zip	19
20	Access, Modify, Enhance: Self-Service Data Management in SAS Visual Analytics	Gregor Herrmann	SAS Visual Analytics provides self-service capabilities for users to analyze, explore, and report on their own data. As users explore their data, there is always a need to bring in more data sources, create new variables, combine data from multiple sources, and even periodically update data. SAS Visual Analytics provides targeted user capabilities to access, modify, and enhance data that is suitable for specific business needs. This paper provides a clear explanation of these capabilities and suggests best practices for self-service data management in SAS Visual Analytics including essential performance considerations	data wrangling, data governance, star schema, fact table, data profiling, data warehouse, dimension table, proc sql, dataset	source	14
21	Adaptive Randomization Using PROC MCMC	Qianyi Huang, John Amrhein	Based on work by Thall et al. (2012), we implemented a Bayesian method for randomizing patients in a Phase II trial. According to this method, each patient is rated for efficacy and toxicity levels at a specified time point after being randomized to a dose. Evidence is accumulated that identifies which dose(s) of a cancer treatment provide the most desirable profile, per a utility score matrix of efficacy and toxicity combinations rated by experts (0-100). Experts also define the region of "Good" utility scores and criteria of dose inclusion based on toxicity and efficacy performance. Simulation work is done mainly using `PROC MCMC` in which priors and likelihood function for joint outcomes of efficacy and toxicity are defined to generate posteriors. Resulting joint probabilities for doses that meet the inclusion criteria are used to calculate the mean utility and probability of having "Good" utility scores	dose, randomization, posterior probability, likelihood function, sample, normal distribution, correlation, prior probability, standard deviation, proc step	source	13
22	Addressing AML Regulatory Pressures by Creating Customer Risk Rating Models with Ordinal Logistic Regression	Edwin Rivera, Jim West	With increasing regulatory emphasis on using more scientific statistical processes and procedures in the Bank Secrecy Act/Anti-Money Laundering (BSA/AML) compliance space, financial institutions are being pressured to replace their heuristic, rule-based customer risk rating models with well-established, academically supported, statistically based models. As part of their customer-enhanced due diligence, firms are expected to both rate and monitor every customer for the overall risk that the customer poses. Firms with ineffective customer risk rating models can face regulatory enforcement actions such as matters requiring attention (MRAs); the Office of the Comptroller of the Currency (OCC) can issue consent orders for federally chartered banks; and the Federal Deposit Insurance Corporation (FDIC) can take similar actions against state-chartered banks	logistic regression, ordinal logistic regression, customer risk rating, logistic distribution, regression analysis, response variable, data cleaning, predictor variable, model validation, model risk management, odds ratio, correlation, sample, machine learning, null hypothesis, spearman correlation, feature selection, aml, forward selection, suspicious activity, likelihood function, proc logistic, maximum likelihood estimation	source	12
23	Advanced Techniques for Fitting Mixed Models Using SAS/STAT Software	Kathleen Kiernan	Fitting mixed models to complicated data, such as data that include multiple sources of variation, can be a daunting task. `SAS/STAT` software offers several procedures and approaches for fitting mixed models. This paper provides guidance on how to overcome obstacles that commo nly occur when you fit mixed models using the MIXED and GLIMMIX procedures. Examples are used to showcase `PROCEDURE OPTIONS` and programming techniques that can help you overcome difficult data and modeling situations	proc step, proc mixed, proc glimmix, proc plm, covariance, fixed effects, covariance structure, repeated measures, correlation, degrees of freedom, confidence interval	source	18
24	Alien Nation: Text Analysis of UFO Sightings in the US Using SAS Enterprise Miner 13.1	Pradeep Reddy Kalakota, Naresh Abburi, Goutam Chakraborty, Zabiulla Mohammed	Are we alone in this universe? This is a question that undoubtedly passes through every mind several times during a lifetime. We often hear a lot of stories about close encounters, Unidentified Flying Objects(UFO) sightings and other mysterious things, but we lack documented evidence for analysis of this topic. The objective of this paper is to analyze one database which has a collection of documented reports of UFO sightings to uncover any fascinating story related to the data. Using SAS Enterprise Miner TM 13.1, the powerful capabilities of text analytics and topic mining are leveraged to summarize the associations between various reported sightings. We have also used `PROC GEOCODE` to convert addresses of sightings to the locations on the map and then used `PROC GMAP` procedure to produce block map to represent the frequency of the sightings in various locations	proc step, create table, proc gmap, data mining, proc freq, hierarchical clustering, variable, parsing, proc sql	source zip	14
25	All In: Integrated Enterprise-Wide Analytics and Reporting with SAS Visual Analytics and SAS Business intelligence	Jonathan Pletzke	In 2013, the University of North Carolina at Chapel Hill (UNC) initiated enterprise-wide use of SAS solutions for reporting and data transformations. Just over one year later, the initial rollout was scheduled to go live to an audience of 5,500 users as part of an adoption of PeopleSoft ERP for Finance, Human Resources, Payroll, and Student systems. SAS Visual Analytics was used for primary report delivery as an embedded resource within the UNC Infoporte, an existing portal. UNC made the date. With the SAS solutions, UNC delivered the data warehouse and initial reports on the same day the ERP systems went live. After the success of the initial launch, UNC continues to develop and evolve the solution with additional technologies, data, and reports. The intent of this presentation is to touch on a few various elements required for a medium to large size organization to integrate SAS solutions such as SAS Visual Analytics within their infrastructure	data warehouse, sas data integration studio	source	11
26	All Payer Claims Databases (APCDs) in Data Transparency and Quality Improvement	Paul A. LaBrec	Since Maine established the first All Payer Claims Database (APCD) in 2003, 10 additional states have established APCDs and 30 others are in development or show strong interest establishing APCDs. APCDs are generally mandated by legislation, though voluntary efforts exist. They are administered through various agencies including state health departments or other governmental agencies and private not-for-profit organizations. APCDs receive funding from various sources including legislative appropriations and private foundations. To ensure sustainability APCDs must also consider the sale of data access and reports as a source of revenue. With the advent of the Affordable Care Act there has been an increased interest in APCDs as a data source to aid in health care reform	insurance claims, health insurance, risk adjustment, sample, insurer, adverse events	source	17
27	Alternative Methods of Regression when OLS is Not Right	Peter L. Flom	Ordinary least square regression is one of the most widely used statistical methods. However, it is a parametric model and relies on assumptions that are often not met. Alternative methods of regression for continuous dependent variables relax these assumptions in various ways. This paper will explore PROCS such as QUANTREG, ADAPTIVEREG and TRANSREG for these data. Keywords: Regression	regression analysis, proc step, proc quantreg, predictor variable, response variable	source	19
28	An Application of the Cox Proportional Hazards Model to the Construction of Objective Vintages for Credit in Financial Institutions, Using PROC PHREG	Iván Darío Atehortua Rojas	Retail segment in a Bank is a major source of incomes. Especially for a bank with a share in the market of 17% in number of credit `CARDS`, the biggest in Colombia. This quantity of lending applications force the bank to use analytic and statistical tools to do an initial selection of good customers . And then analyze each of those applications that had the lowest probability of default and will imply secure incomes to the organization. Logit models are used f or the construction of acquisition models. Where t he objective is to estimate the probability that in a certain fixed period of time, usually 12 months, the customer falls into default. Besides, to know the probability that a customer falls into default in different time periods (e.g. one, two, … , n months after the entailment) is necessary for credit risk vintage analysis. Hence Cox Proportional Hazards M odel becomes important. Because w ith this model, probabilities are more predictive	logistic regression, survival function, credit risk, survival analysis, response variable, proc phreg, binomial distribution	source zip	8
29	An Application of the DEA Optimization Methodology to Make More Effective and Efficient Collection Calls	Jenny Lancheros, Ana Nieto	In our Management Collection Area, there was no methodology that provided the optimal number of collection calls to get the customer to make the minimum payment of his or her financial obligation. We wanted to determine the optimal number of calls using th e data envelopment analysis (DEA) optimization methodology. Using this methodology, we obtained results that positively impacted the way our customers were contacted. We can maintain a healthy bank and customer relationship, keep management and collection at an operational level, and obtain a more effective and efficient portfolio recovery. The DEA optimization methodology has been successfully used in various fields. It has solved multi-criteria optimization problems, but it has not been commonly used in t he financial sector, especially in the collection area. This methodology requires specialized software, such as SAS Enterprise Guide	days past due, credit card	source	5
30	An Empirical Comparison of Multiple Imputation Approaches for Treating Missing Data in Observational Studies	Patricia Rodríguez de Gil, Shetay Ashford, Chunhua Cao, Eun Sook Kim, Rheta Lanehart, Reginald Lee, Jessica Montgomery, Yan Wang	Missing data are a common and significant problem that researchers and data analysts encounter in applied research. Because most statistical procedures require complete data, missing data c an substantially affect the analysis and the interpretation of results if left untreated. Methods to treat missing data have been developed so that missing values are imputed and analyses can be conducted using standard statistical procedures. Among these missing data methods, Multiple Imputation has received considerable attention and its effectiveness has been explored, for example, in the context of survey and longitudinal research. This paper compares four Multiple Imputation approaches for treating mis sing continuous covariate data under MCAR, MAR, and MNAR assumptions in the context of propensity score analysis and observational studies	type i error, missing value, treatment effect, correlation, imputation, type i error control, confidence interval, significance level, multiple imputation, power analysis, proc mi, propensity score, sample, proc step, missing not at random, missing at random	source	20
31	An Innovative Method of Customer Clustering	Brian Borchers, Ashlie Ossege	This paper describes an innovative way to identify groupings of customer offerings using SAS software. The authors investigated the customer enrollments in nine different programs offered by a large energy utility. These programs included levelized billing plans, electronic payment options, renewable energy, energy efficiency programs, a home protection plan, and a home energy report for managing usage. Of the 640,788 residential customers, 374,441 had been solicited for a program and had adequate data for analysis. Nearly half of these eligible customers (49.8%) enrolled in some type of program. To examine the commonality among programs based on characteristics of customers who enroll, cluster analysis procedures and correlation matrices are often used. However, the value of t hese procedures was greatly limited by the binary nature of enrollments (enroll or no enroll), as well as the fact that some programs are mutually exclusive (limiting cross-enrollments for correlation measures)	logistic distribution, cluster analysis, correlation, proc step, logistic regression, proc fastclus, proc logistic, predictor variable, regression analysis, data cleaning, proc princomp	source	6
32	An Insider's Guide to ODS LAYOUT Using SAS 9.4	Scott Huntley	Whenever you travel, whether it's to a new destination or your favorite vacation spot, it's nice to have a guide to assist you with planning and setting expectations. The `ODS` LAYOUT statement became production in SAS 9.4. For those intrepid programmers who used `ODS` LAYOUT in an earlier release of SAS this paper contains tons of information about changes you need to know about. Programmers new to SAS 9.4 (or new to `ODS` LAYOUT) need to understand the basics. This paper reviews some common issues customers have reported to SAS Technical Support when migrating to the LAYOUT destination in SAS 9.4 and explores the basics for those who are making their first foray into the adventure that is `ODS` LAYOUT. This paper discusses some tips and tricks to ensure your trip through the `ODS` LAYOUT statement will be a fun and rewarding one	output delivery system, proc step, proc report, ods pdf, proc print, sas enterprise guide, options statement	source	35
33	An Introduction to Testing for Unit Roots Using SAS: The Case of U.S. National Health Expenditures	Don McCarthy	Determining whether a dataset is stationary can be important for the economist who does empirical work and SAS allows the user to test for a unit root using several different tests: the Augmented Dickey -Fuller, Phillips-Perron, and the Kwiatkowski-Schmidt-Shin tests can all be implemented using `PROC AUTOREG`. This paper presents a brief, non-technical, overview of unit roots and shows how to test for a unit root and stationarity in U.S. health expenditure data	unit root, null hypothesis, first difference, time series analysis, covariance, sample, regression analysis	source zip	6
34	An Introductory Overview of the Features of Complex Survey Data	Taylor Lewis	A "complex" survey data set is one characterized by any combination of the following four features: stratification, clustering, unequal weights, or finite population correction factors. In this paper, context is provided as to why these features may appear in data sets produced from surveys, some of the formulaic modifications they introduce are highlighted, and the syntax needed to properly account for them is outlined. Specifically, we explain why you should utilize the SURVEY family of `SAS/STAT` procedures, such as `PROC SURVEYMEANS` or `PROC SURVEYREG`, to analyze data of this type	sample, proc step, proc surveymeans, sample size, proc calis, random sampling, population, design effect, response variable, confidence interval, finite population correction, sample weight, cluster sampling, credit card, dataset	source	22
35	An Overview of Econometrics Tools in SAS/ETS: Explaining the Past and Modeling the Future:	Mark Little, Kenneth Sanford	The importance of econometrics in the analytics toolkit is growing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the `SAS/ETS` software portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more	time series analysis, price elasticity, proc freq, regression analysis, response variable, factor loading, panel data	source	13
36	Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer	Patricia A. Berglund	This paper provides an overview of analysis of data derived from complex sample designs. General discussion of how and why analysis of complex sample data differs from standard analysis is included. In addition, a variety of applications are presented using `PROC SURVEYMEANS`, `PROC SURVEYFREQ`, `PROC SURVEYREG`, `PROC SURVEYLOGISTIC`, and `PROC SURVEYPHREG`, with an emphasis on correct usage and interpretation of results	proc step, sample, proc calis, regression analysis, proc surveymeans, proc surveyreg, logistic regression, proc surveyfreq, logistic distribution, data science, proc reg, proc surveylogistic, chi-square test, random sampling, box plot, survival analysis, proc freq, confidence interval, response variable, sample size, by group processing, format statement, hazard ratio, null hypothesis, proc means, standard deviation, histogram	source	19
37	Analytics to Inform Name Your Own Price Reserve Setting	Alison Burnham	ScoreBig.com has a diverse array of information available to it to price inventory. The Name Your Own Price format allows insight into not only the binary information usually available for pricing but also an amount the buyer is willing to pay. This essential aspect of the ScoreBig site has meant that analytics around pricing data has been a key focus of the business from the very begin ning. This paper discusses the analytics that are done and provides an example of analytics around Major League Baseball pricing		source	3
38	Analyzing and Visualizing the Sentiment of the Ebola Outbreak via Tweets	Dheeraj Jami, Goutam Chakraborty, Shivkanth Lanka	The Ebola virus outbreak has produced some of the most significant and fastest trending news throughout the globe today. There has been a lot of buzz surrounding the deadly disease and the drastic consequences that it potentially poses to mankind. Social media provides the basic platforms for millions of people to discuss the issue, and allows them to openly voice their opinions. There has been a significant increase in the magnitude of responses all over the world since the death of an Ebola patient in a Dallas, Texas hospital. In this pap er, we aim to analyze the overall sentiment that is prevailing in the world of media. For this, we extracted the live streaming data from Twitter over time and studied its pattern based on the Ebola Timeline	data mining, data cleaning, sample	source	11
39	Analyzing Customer Answers in Calls from Collections Using SAS Text Miner to Respond in an Efficient and Effective Way	Oscar Ayala, Jenny Lancheros	At the Multibanca Colpatria of Scotiabank, we offer a broad range of financial services and products in Colombia. In collection management, we currently manage more than 400,000 customers each month. In the call center, agents collect answers from each contact with the customer, and this information is saved in databases. However, this information has not been explored to know more about our customers and our own operation. The objective of this paper is to develop a classification model using the words in the answers from each custo mer from the call about receiving payment. Using a combination of text mining and cluster methodologies, we identify the possible conversations that can occur in each stage of delinquency. This knowledge makes developing specialized scripts for collection management possible. Key words: Text Mining, Segmentation, Collect, document	data mining, binary variable	source	6
40	Analyzing Direct Marketing Campaign Performance Using Weight of Evidence Coding and Information Value through SAS Enterprise Miner Incremental Response	Sravan Vadigepalli	Data Mining and predictive models are extensively used to find the optimal customer targets so as to maximize the return on investment. Direct marketing techniques target all the customers who are likely to buy regardless of the customer classification. In a real sense this mechanism couldn't classify the customers who are going to buy even without a marketing contact, there by resulting in a loss on investment. This paper focuses on Incremental Lift modeling approach using Weight of Evidence Coding and Inf ormation Value followed by Incremental Response and Outcome model Diagnostics. This model identifies the additional purchases that would not have taken pla ce without a Marketing campaign. Modeling work was conducted using combined model. The research work is carried out on a Travel Center data, which identifies the increase in average response rate by 2.8% and the number of fuel gallons by 244 when compared with the results from the traditional campaign, which targeted everyone	data cleaning, validation set, null hypothesis, significance level, data mining	source zip	9
41	Analyzing Marine Piracy from Structured and Unstructured Data Using SAS Text Miner	Raghavender Reddy Byreddy, Nitish Byri, Goutam Chakraborty, Tejeshwar Gurram, Anvesh Reddy Minukuri	Approximately 80% of world trade at present uses the seaways, with around 110,000 merchant vessels and 1.25 million marine farers transporting almost 6 billion tons of goods every year. Marine piracy stands as a s erious challenge to sea trade. Understanding how the pirate attacks occur is crucial in effectively countering marine piracy. Predictive modeling using the combination of textual data with numeric data provides an effective methodology to derive insights f rom both structured and unstructured data. 2,266 text descriptions about pirate incidents that occurred over the past seven years, from 2008 to the second quarter of 2014 were collected from the International Maritime Bureau (IMB) website	response variable, unstructured data	source	12
42	Analyzing Messy and Wide Data on a Desktop	John P. Sall	Analysis work can be challenged by data that is too wide, too full of miscodings, outliers, or holes, or by funny data types. Wide data, in particular, has many challenges, requiring the analysis to adapt with different methods. Making covariance matrices with billions of elements is just not practical.	missing value, discriminant analysis, covariance, imputation, outlier	source	4
43	Analyzing Parking Meter Transactions Using SAS Procedures	Anders Milhøj	The analysis is based upon data for all transactions at four parking meters within a small area in central Copenhagen for a period of four years. The observations are the exact time of the transaction and the number of minutes for parking bought for each transaction. These series of at most 80,000 transactions are aggregated to hours, days, weeks and months using `TIMESERIES` procedure. The aggregated series of parking times and numbers of transactions are then analyzed for seasonality and interdependence by X12, UCM and VARMAX procedures	proc step, time series analysis, proc x12, proc varmax, seasonal adjustment, regression analysis, autoregressive model, proc ucm, feature selection, out=, outlier	source	12
44	Analyzing Spatial Point Patterns Using the New SPP Procedure	Pradeep Mohan, Randy Tobias	In many spatial analysis applications (including crime analysis, epidemiology, ecology, and forestry), spatial point process modeling can help you study the interaction between different events and help you model the process intensity (the rate of event occurrence per unit area). For example, crime analysts might want to estimate where crimes are likely to occur in a city and whether they are associated with locations of public features such as bars and bus stops. Forestry researchers might want to estimate where trees grow best and test for association with covariates such as elevation and gradient. This paper describes the `SPP` procedure, new in `SAS/STAT` 13.2, for exploring and modeling spatial point pattern data. It describes methods that `PROC SPP` implements for exploratory analysis of spatial point patterns and for log-linear intensity modeling that uses covariates	proc step, poisson distribution, proc means	source	20
45	Anatomy of a Merge Gone Wrong	Joshua M. Horstman	The merge is one of the SAS programmer's most commonly used tools. However, it can be fraught with pitfalls to the unwary user. In this paper, we look under the hood of the `DATA STEP` and examine how the program data vector works. We see what's really happening when datasets are merged and how to avoid subtle problems	merge statement, pdv, by group processing, out=	source	5
46	Application of Text Mining on Tweets to Collect Rational Information about Type-2 Diabetes	Shubhi Choudhary, Goutam Chakraborty, Vijay Singh	Twitter is a powerful form of social media for sharing information about various issues and can be used to raise awareness and collect pointers about associated risk factors and preventive measures. Type -2 diabetes is a national problem in the US. We analyzed twitter feeds about Type -2 diabetes in order to suggest a possible use of social media with respect to a study of any ailment. To accomplish this task, 3.488 tweets were collected using Twitter API v1.1 in a customized Python script. Tweets, follower counts, and user information were extracted via these scripts. The tweets were segregated into different groups on the basis of their annotations related to risk factors, complications, preventions and precautions, and so on. We then used SAS Text Miner to analyze the data. We found that 70% of the tweets stated the status quo based on marketing and awareness campaigns. The remaining 30% of tweets contained various key terms and labels associated with type -2 diabetes	proc freq, data dictionary	source	7
47	Applied Analytics in Festival Tourism: A Case Study of Intention-to-Revisit Prediction in an Annual Local Food Festival in Thailand	Jongsawas Chongwatpol, Thanathorn Vajirakachorn	Improving tourists' satisfaction and intention to revisit the festival is an ongoing area of interest to tourism industry. Many organizers at the festival site strive very hard to attract and retain attendees by investing heavily in their marketing and promotion strategies for the festival. To meet this challenge, the advanced analytical model though data mining approach is proposed to answer the following research question: "What are the most important factors that influence tourists' intention to revisit the festival site?" Cluster analysis, neural network, decision tree, stepwise regression, polynomial regression, and support vector machine are applied in this study. The main goal is to determine what it takes not only to retain the loyalty attendees, but also attract and encourage new attendees to be back at the site	regression analysis, neural network, proc hpsplit, polynomial regression, support vector machine, type ii error, logistic distribution, cluster analysis, data mining, logistic regression, response variable, network optimization, predictor variable, stepwise selection, %if-%then-%else	source	13
48	Are You a Control Freak? Control Your SAS Programs - Don't Let Them Control You!	Mary F. O. Rosenbloom, Arthur L. Carpenter	You know that you want to control the process flow of your program. When your program is executed multiple times, with slight variations, you will need to control the changes from iteration to iteration, the timing of the execution, and the maintenance of output and LOGS. Unfortunately in order to achieve the control that you know that you need to have, you will need to make frequent, possibly time consuming, and potentially error prone manual corrections and edits to your program. Fortunately the control you seek is available and it does not require the use of time intensive manual techniques. List processing techniques are available that give you control and peace of mind that allow you to be a successful control freak. These techniques are not new, but there is often hesitancy on the part of some programmers to take full advantage of them. This paper reviews these techniques and demonstrates them through a series of examples	macro variable, macro loop, do loop, proc step, variable list, macro call, proc sql, macro put, proc report, excel, macro quoting function	source	20
49	Assessing the Impact of Communication Channel on Behavior Changes in Energy Efficiency	Angela Wells, Ashlie Ossege	With the increase in government and commissions incentivizing electric utilities to get consumers to save energy, there has been a large increase in the number of energy saving programs. Some are structural, incentivizing consumers to make improvements to their home that result in energy savings. Some, called behavioral programs, are designed to get consumers to change their behavior to save energy. Within behavioral programs, Home Energy Reports are a good method to achieve behavioral savings as well as to educate consumers on structural energy savings. This paper examines the differen t Home Energy Report communication channels (direct mail and e-mail) and the marketing channel effect on energy savings, using SAS for linear models	proc step, treatment period, proc genmod, proc glm, proc fastclus	source	14
50	Automated Macros to Extract Data from the National (Nationwide) Inpatient Sample (NIS)	Ravi Tejeshwar Reddy Gaddameedi, Usha Kreaden	The use of administrative databases for understanding practice patterns in the real world has become increasing apparent. This is essential in the current healthcare environment. The Affordable Healthcare Act has created the need to probe further into the current usage of technology and into the use of different approaches to surgery. This paper describes a method for extracting surgical procedure-specific information from the Healthcare Cost and Utilization Project (HCUP) database, otherwise referred to as the National (Nationwide) Inpatient Sample (NIS). Using this systematic method in SAS 9.2, you will be able to extract data from NIS using the ARRAY statement for the specific ICD-9 codes, to format the extracted data for analysis, to merge the different NIS databases by procedures, and to use automatic macros to generate a report. These analyses provide a framework for comparing key outcome variables for different modalities of surgery for procedures of interest	array, format statement	source	7
51	Automating a SAS 9.4 Installation without Using Provisioning Software: A Case Study Involving the Setup of Machines for SAS Regional Users Groups	Max Blake	Whether you manage computer systems in a small-to-medium environment (for example, in labs, workshops, or corporate training groups) or in a large-scale deployment, the ability to automate SAS 9.4 installations is important to the efficiency and success of your software deployments. For large-scale deployments, you can automate the installation process by using third-party provisioning software such as Microsoft System Center Configuration Manager (SCCM) or Symantec Altiris. But what if you have a small-to-medium environment and you do not have provisioning software to package deployment jobs? No worries! There is a solution. This paper presents a case study of just such a situation where a process was developed for SAS regional users groups (RUGs). Along with the case study, the paper offers a process for automating SAS 9.4 installations in workshop, lab, and corporate training (small-to-medium sized) environments	sas enterprise guide, proc print, sas studio, excel	source	19
52	Automating Your Metadata Reporting	Rupinder Dhillon, Darryl Prebble	Keeping track of which of your SAS Data Integration Studio (DI Studio) programs are updating which tables, or even where your programs are stored can be a full-time job in itself. Even the best of intentions tend to fall out of date the moment things get organized and deployed. What if there was a way to automate your metadata reporting to keep it current, and also create an audit trail of history while you're at it?	sas data integration studio, sas enterprise guide	source	6
53	Automation of Statistics Summary for Census Data in SAS	Janet Lee	Presentation	by group processing, sample, sample size	source	5
54	Benefits from the Conversion of Medicaid Encounter Data Reporting System to SAS Enterprise Guide	Eric Sather	Presentation	sas enterprise guide, data validation rules, insurance claims, excel	source	4
55	Best Practice for Creation and Maintenance of a SAS Infrastructure	Paul Thomas	The advantage of using metadata to control and maintain data and access to data on databases, marts and warehouses is well understood. SAS Metadata Server, SAS Management Console and other associated products provide a facility for hosting this metadata, however the metadata itself also needs managing. This paper, aimed at individuals new to SAS administration or who are looking for r ules to help tidy up an existing installation, provides some best practice methods and tips to ensure well controlled and managed metadata. This includes suggestions for folder structures, access rights, environments and promotion, SAS DI Studio jobs and flows	libname statement, macro variable, sas enterprise guide, sas data integration studio	source	8
56	Best Practices for Configuring Your I/O Subsystem for SAS9 Applications	Tony Brown, Margaret A. Crevar	The power of SAS 9 applications allows information and knowledge creation from very large amounts of data. Analysis that used to consist of 10s-100s of gigabytes (GBs) of supporting data has rapidly grown into the 10s to 100s of terabytes (TBs). This data expansion has resulted in more 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 stora ge 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, business intelligence	source	9
57	Best Practices for Upgrading from SAS 9.1.3 to SAS 9.4	David Stern	We regularly speak with organizations running established SAS 9.1.3 systems that have not yet upgraded to a later version of SAS. Often this is because their current SAS 9.1.3 environment is working fine, and no compelling event to upgrade has materialized. Now that SAS 9.1.3 has moved to a lower level of support and some very exciting technologies (Hadoop, cloud, ever -better scalability) are more accessible than ever using SAS 9.4, the case for migrating from SAS 9.1.3 is strong. Upgrading a large SAS ecosystem with multiple environments, an active development stream, and a busy production environment can seem daunting. This paper aims to demystify the process, suggesting outline migration approaches for a variety of the most common scenarios in SAS 9.1.3 to SAS 9.4 upgrades, and a scalable template project plan that has been proven at a range of organizations	sas management console, spk file, big data, sas enterprise guide, data cleaning	source	20
58	Best Practices: Subset Without Getting Upset	Mary F. O. Rosenbloom, Kirk Paul Lafler	You've worked for weeks or even months to produce an analysis suite for a project, and at the last moment, someone wants a subgroup analysis and they inform you that they need it yesterday. This should be easy to do, right? So often, the programs that we write fall apart when we use them on subsets of the original data. This paper takes a look at some of the best practice techniques that can be built into a program at the beginning, so that users can subset on the fly without losing categories or creating errors in statistical tests. We review techniques for creating tables and corresponding titles with by-group processing so that minimal code needs to be modified when more groups are created, and we provide a link to sample code and sample data that can be used to get started with this process	proc step, proc means, proc sql, proc format, macro variable, proc freq, standard deviation, class variable, subgroup analysis, missing value, proc report, proc tabulate, by group processing, out=, macro loop, do loop, where statement, nominal variable, sample	source	17
59	Better Metadata Through SAS: %SYSFUNC, PROC DATASETS, and Dictionary Tables	Louise S. Hadden	SAS provides a wealth of resources for users to create useful, attractive metadata tables, including `PROC CONTENTS` listing output (to `ODS` destinations), the `PROC CONTENTS` OUT= SAS data set, and `PROC CONTENTS` `ODS` Output Objects. This paper and presentation explore some less well-known resources to create metadata such as %SYSFUNC, `PROC DATASETS`, and Dictionary Tables. All these options in conjunction with the use of the EXCELXP (and new in 9.4 M2, the `EXCEL`) `TAGSETS` enable the creation of multi-tab metadata workbooks at the click of a mouse	proc step, proc contents, proc sql, macro function, macro variable, dictionary tables, data step	source	7
60	BI-on-BI for SAS Visual Analytics	Murali Nori	SAS Visual Analytics is deployed by many customers. IT departments are tasked with efficiently managing the server resources, achieving maximum usage of resources, optimizing availability, and managing costs. Business users expect the system to be available when needed and to perform to their expectations. Business executives who sponsor business intelligence (BI) and analytical projects like to see that their decision to support and finance the project meets business requirements. Business executives also like to know how different people in the organization are using SAS Visual Analytics. With the release of SAS Visual Analytics 7.1, new functionality is available to support the memory management of the SAS LASR Analytic Server. Also, new, out-of-the-box usage and audit reporting is available. This paper covers BI-on-BI for SAS Visual Analytics. We discuss resource management, data compression, and out-of-the-box usage reporting of SAS Visual Analytics	data wrangling, bar chart	source	9
61	Big Data: Have You Seen It? Using Graph Theory to Improve Your Analytics: Hypergroup Action	Yue Qi, Trevor D. Kearney	Your data analysis projects can use the SAS LASR Analytic Server and its new HYPERGROUP functionality to mine relationships using graph theory. Discover which data entities are related and, conversely, which sets of values are disjoint. In cases when the sets of values are not completely disjoint, HYPERGROUP can identify data that is strongly connected and identify "neighboring" data that is weakly connected, or data that is a greater distance away. Each record is assigned a hypergroup number and, within hypergroups, is assigned a color, community, or both. The GROUPBY facility, WHERE clauses, or both can act on a hypergroup's number, color, or community to conduct analytics using data that is "close", "related", or more "relevant". The algorithms that are used to determine hypergroups are based on graph theory	scatter plot, proc step, where statement	source	31
62	Building a Template from the Ground Up with Graph Template Language	Jedediah J. Teres	This paper focuses on building a graph template in an easy-to-follow, step-by-step manner. The paper begins with using Graph Template Language to re-create a simple series plot, and then moves on to include a secondary y-axis as well as multiple overlaid block plots to tell a more complex and complete story than would be possible using only the `SGPLOT` procedure	series plot, proc step, proc template, proc sgplot, graph template language	source	21
63	Building Credit Modeling Dashboards	Jeremy D'Antoni	Presentation	model governance, unique key, operational risk, key performance indicator, sas base, excel	source	7
64	Building Flexible Jobs in DataFlux	Jeanne Estridge	Creating DataFlux jobs that can be executed from job scheduling software can be challenging. This paper describes how to create a process job that calls an email node that accepts a distribution list, subject line, email text, and attachment file name as macro variables. It also explains how to execute this job from a command line, overriding those values at run-time	macro variable, job scheduling	source	13
65	Bust Open That ETL Black Box and Apply Proven Techniques to Successfully Modernize Data Integration	Brandon Kirk	So you are still writing SAS DATA steps and SAS macros and running them through a command -line scheduler. When work comes in, there is only one person who knows that code, and they are out -what to do? This paper shows how SAS applies extract, transform, and load (ETL) modernization techniques with SAS Data Integration Studio to gain resource efficiencies and to break down the ETL black box. We are going to share the fundamentals (metadata folder organization and naming standards) that ensure success, along with steps to ease into the pool while iteratively gaining benefits. Benefits include self-documenting code visualization, impact analysis on jobs and tables to identify objects impacted by change, and being supportable by interchangeable bench resources. We concl ude with demonstrating how SAS Visual Analytics is being used to monitor service-level agreements and provide actionable insights into job-flow performance and scheduling	data governance, data integration, big data	source	12
66	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, missing value, trailing @, character variable, date variable, flat file, dataset, length statement	source	20
67	Catering to Your Tastes: Using PROC OPTEX to Design Custom Experiments, with Applications in Food Science and Field Trials	Cliff Pereira, Randy Tobias	The success of an experimental study almost always hinges on how you design it. Does it provide estimates for everything you're interested in? Does it take all the experimental constraints into account? Does it make efficient use of limited resources? The `OPTEX` procedure in `SAS/QC` software enables you to focus on specifying your interests and constraints, and it takes responsibility for handling them efficiently. With `PROC OPTEX`, you skip the step of rifling through tables of standard designs to try to find the one that's right for you. Y ou concentrate on the science and the analytics and let SAS do the computing. This paper reviews the features of `PROC OPTEX` and shows them in action using examples from field trials and food science experimentation. `PROC OPTEX` is a useful tool for all these situations, doing the designing and freeing the scientist to think about the food and the biology	proc step, fixed effects	source zip	11
68	Causal Analytics: Testing, Targeting, and Tweaking to Improve Outcomes	Jason Pieratt	Causal Analytics is the business application of statistical methods that are used for modeling causal relationships between inputs, interventions, and outcomes. Three areas of Causal Analytics are introduced in this paper: "testing" whether outcomes change because of an intervention; "targeting" interventions to have the greatest improvement in outcomes; and "tweaking" an intervention to control system outcomes	causal inference, treatment effect, propensity score, adverse events	source	8
69	CHARACTER DATA: Acquisition, Manipulation, and Analysis	Andrew T. Kuligowski, Swati Agarwal	The `DATA STEP` enables you to read, write, and manipulate many types of data. As data evolves to a more free-form state, the ability of SAS to handle character data becomes increasingly important. This presentation, expanded and enhanced from an earlier version, addresses character data from multiple vantage points. For example, what is the default length of a character string, and why does it appear to change under different circumstances? Special emphasis is given to the myriad functions that can facilitate the processing and manipulation of character data. This paper is targeted at a beginning to intermediate audience	character variable, length statement, character functions, macro variable, upcase function	source	13
70	Chi-Square and T-Tests Using SAS: Performance and Interpretation	Jennifer L. Waller, Maribeth H. Johnson	Data analysis begins with data clean up, calculation of descriptive statistics and the examination of variable distributions. Before more rigorous statistical analysis begins, many statisticians perform basic inferential statistical tests such as chi-square and t-tests to assess unadjusted associations. These tests help to guide the direction of the more rigorous analysis. How to perform chi-square and t-tests will be presented. We will examine how to interpret the output, where to look for the association or difference based on the hypothesis being tested, and propose next steps for further analysis using example data	sample, nominal variable, chi-square test, interval variable, proc means, proc ttest, proc step, odds ratio, data science, standard deviation, confidence interval, proc freq, relative risk, goodness of fit, inferential statistics, normal distribution, homoscedasticity, missing value, box plot, one-sample t-test, heteroscedasticity, variable list, frequency analysis, ordinal variable, histogram, class variable	source	12
71	Clustering Techniques to Uncover Relative Pricing Opportunities: Relative Pricing Corridors Using SAS Enterprise Miner and SAS Visual Analytics	Ryan K. Carr, Charles Park	The impact of price on brand sales is not always linear or independent of other brand prices. We demonstrate, using sales information and SAS Enterprise Miner, how to uncover relative price bands where price can be increased without losing market share or decreased slightly to gain market share	data mining, scatter plot, bubble plot	source	8
72	Coding Your Own MCMC Algorithm	Chelsea Lofland	In Bayesian statistics, Markov chain Monte Carlo (MCMC) algorithms are an essential tool for sampling from probability distributions. `PROC MCMC` is useful for these algorithms, however it is often desirable to code an algorithm from scratch. This is especially present in academia where students are expected to be able to understand and code an MCMC. The ability of SAS to accomplish this is relatively unknown yet quite straightforward. We use SAS `IML` to demonstrate methods for coding an MCMC algorithm with examples of a Gibbs sampler and Metropolis- Hastings random walk	sample, proc step, random number generation, histogram	source	19
73	Colon(:)izing My Programs	Jinson J. Erinjeri	There is a plethora of uses of the colon (:) in SAS programming. The colon is used as a data/variable name wild card, a macro variable creator, an operator modifier, and so forth. The usage of the colon helps in writing clear, concise and compact codes. The main objective of this paper is to encourage the effective use of the colon in writing crisp codes. This paper presents the real time scenarios of application of the colon in day to day programming. In addition, this paper also presents cases where the colon limits the programmer's wish	colon modifier, macro variable, proc step, goto	source	7
74	Competing Risk Survival Analysis Using SAS: When, Why, and How	Lovedeep Gondara	Competing risk arise in time to event data when the event of interest cannot be observed because of a preceding event i.e. a competing event occurring before. An example can be of an event of interest being a specific cause of death where death from any other cause can be termed as a competing e vent, if focusing on relapse, death before relapse would constitute a competing event. It is well studied and pointed out that in presence of competing risks, the standard product limit methods yield biased results due to violation of their basic assumption. The effect of competing events on parameter estimation depends on their distribution and frequency. Fine and Gray's sub-distribution hazard model can be used in presence of competing events which is available in `PROC PHREG` with the release of version 9.4 of SAS software	competing risks, proc phreg, survival analysis, proc freq, proc step	source	10
75	Confirmatory Factor Analysis Using PROC CALIS: A Practical Guide for Survey Researchers	Lindsey Brown Philpot, Sunni Barnes, Crystal Carel	Survey research can provide a straightforward and effective means of collecting input on a range of topics. Survey researchers often like to group similar survey items into construct domains in order to make generalizations about a particular area of interest. Confirmatory Factor Analy sis is used to test whether this pre-existing theoretical model underlies a particular set of responses to survey questions. Based on Structural Equation Modeling (SEM), Confirmatory Factor Analysis provides the survey researcher with a means to evaluate how well the actual survey response data fits within the a priori model specified by subject matter experts. `PROC CALIS` now provides survey researchers the ability to perform Confirmatory Factor Analysis using SAS. This paper provides a survey researcher with the steps needed to complete Confirmatory Factor Analysis using SAS	proc calis, proc step, confirmatory factor analysis, factor analysis, correlation, array, covariance	source	9
76	Converting Annotate to ODS Graphics. Is It Possible?	Philip R. Holland	In the previous chapter I described how many standard `SAS/GRAPH` plots can be converted easily to `ODS` Graphics, by using simple `PROC SGPLOT` or SGPANEL code. `SAS/GRAPH` Annotate code would appear, at first sight, to be much more difficult to convert to `ODS` Graphics, but, making use of its layering features, many Annotate plots can be replicated in a more flexible and repeatable way. This chapter will explain how to convert many of your Annotate plots, so they can be reproduced using Base SAS	proc step, proc sgplot, sample, proc gplot, scatter plot	source	14
77	Count Series Forecasting	Michael J. Leonard	Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are few forecasting techniques (Croston's method and others). This paper proposes a technique for large-scale automatic count series forecasting and uses SAS Forecast Server and `SAS/ETS` software to demonstrate this technique	time series analysis, probability distribution, sample, feature selection, time series data, residual, confidence interval, proc esm, poisson distribution, statistic, proc timeseries, proc step, binomial distribution, forecast horizon, likelihood function, seasonality	source	14
78	Creating Multi-Sheet Microsoft Excel Workbooks with SAS: The Basics and Beyond Part 2	Vincent DelGobbo	This presentation explains how to use Base SAS 9 software to create multi-sheet `EXCEL` workbooks. You learn step-by-step techniques for quickly and easily creating attractive multi-sheet `EXCEL` workbooks that contain your SAS output using the ExcelXP Output Delivery System (`ODS`) tagset. 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	excel, xls, sas output, sas base, ods xml, proc report, sas log, proc tabulate, sample, adverse events, format statement, dataset	source zip	21
79	Creating Reports in SAS Visual Analytics Designer That Dynamically Substitute Graph Roles on the Fly Using Parameterized Expressions	Kenny Lui	With the expansive new features in SAS Visual Analytics 7.1, you can now take control of the graph data while viewing a report. Using parameterized expressions, calculated items, custom categories, and prompt controls, you can now change the measures or categories on a graph from a mobile device or web viewer. View your data from different perspectives while using the same graph. This paper demonstrates how you can use these features in SAS Visual Analytics Designer to create reports in which graph roles can be dynamically changed with the click of a button	bar chart, proc freq, if-then-else	source	12
80	Creative Uses of Vector Plots Using SAS	Deli Wang	Hysteresis loops occur because of a time lag between input and output. In pharmaceutical industry, hysteresis plots are tools to visualize the time lag between drug concentration and drug effects. Before SAS 9.2, SAS annotations can be used to generate such plots. One of the criticisms is that SAS programmers have to write complex macros to automate the process. Code management and validation tasks are not easy. With SAS 9.2, SAS programmers are able to generate such plots with ease. This paper demonstrates the generation of such plots with both SAS annotation and SAS Graph 9.2	sas programmer, proc sgplot, proc step	source	8
81	Credit Card Holders' Behavior Modeling: Transition Probability Prediction with Multinomial and Conditional Logistic Regression in SAS/STAT	Denys Osipenko, Jonathan Crook	Because of the variety of card holders' behavior patterns and income sources, each consumer account can change to different states. Each consumer account can change to states such as non -active, transactor, revolver, delinquent, and defaulted, and each account requires an individual model for generated income prediction. The estimation of the transition probability between statuses at the account level helps to avoid the lack of memory in the MDP approach. The key question is which approach giv es more accurate results: multinomial logistic regression or multistage decision tree with binary logistic regressions. This paper investigates the approaches to credit `CARDS`' profitability estimation at the account level based on multistates conditional p robability by using the `SAS/STAT` procedure `PROC LOGISTIC`. Both models show moderate, but not strong, predictive power. Prediction accuracy for decision tree is dependent on the order of stages for conditional binary logistic regression	logistic regression, regression analysis, logistic distribution, credit card, multinomial logistic regression, proc hpsplit, conditional logistic regression, interest, proc logistic, sample, days past due, proc step, delinquency, expected loss, fee income, past due, transition matrix, data cleaning	source	13
82	Credit Scorecard Generation Using the Credit Scoring Node in SAS Enterprise Miner	Ayush Priyadarshi, Kushal Kathed, Shilpi Prasad	Presentation	credit risk, data mining, data cleaning, scorecard	source	3
83	Cutpoint Determination Methods in Survival Analysis Using SAS: Updated %FINDCUT Macro	Jayawant N. Mandrekar, Jeffrey P. Meyers	Statistical analysis that uses data from clinical or epidemiological studies, include continuous variables such as patient's age, blood pressure, and various biomarkers. Over the years there has been increase in studies that focus on assessing associations between biomarkers and disease of interest. Many of the biomarkers are measured as continuous variables. Investigators seek to identify the possible cutpoint to classify patients as high risk versus low risk based on the value of the biomarker. Several data -oriented techniques such as median and upper quartile, and outcome-oriented techniques based on score, Wald and likelihood ratio tests are commonly used in the literature. Contal and O'Quigley (1999) presented a technique that used log rank test statistic in order to estimate the cutpoint. Their method was computationally intensive and hence was overlooked due to the unavailability of built in options in standard statistical software	interval variable, hazard ratio, regression analysis, likelihood function, macro call, proc phreg, graph template language, data step, array, macro parameter, proc means	source	8
84	Dallas County Midterm Election Exit Polling Research	Yusun Xia	Presentation		source	3
85	Data Aggregation Using the SAS Hash Object	Paul M. Dorfman, Donald J. Henderson	Soon after the advent of the SAS hash object in Version 9.0, its early adopters realized that the pot ential functionality of the new structure is much broader than basic O(1)-time look-up and file matching. Specifically, they went on to inventing methods of data aggregation based on the ability of the hash object to quickly store and update key summary information. They also demonstrated that the `DATA STEP` aggregation using the hash object offered significantly lower run time and memory utilization compared to the SUMMARY/MEANS or SQL procedures, coupled with the possibility of eliminati ng the need to write the aggregation results to interim data files and programming flexibility allowing to combine sophisticated data manipulation and adjustments of the aggregates within a single step	hash table, composite key, where statement, proc sql, sas log, memory usage, sample, insurance claims	source	20
86	Data Management Techniques for Complex Healthcare Data	Gabriela Cantu, Christopher Klekar	Data sharing through healthcare collaboratives and national registries creates opportunities for secondary data analysis projects. These initiatives provide data for quality comparisons as well as endless research opportunities to external researchers across the country. The possibilities are bountifu l when you join data from diverse organizations and look for common themes related to illnesses and patient outcomes. With these great opportunities comes great pain for data analysts and health services researchers tasked with compiling these data sets according to specifications. Patient care data is complex, and, particularly at large healthcare systems, might be managed with multiple electronic health record (EHR) systems. Matching data from separate EHR systems while simultaneously ensuring the integri ty of the details of that care visit is challenging	proc sql, proc step, data merging, proc format, lookup, max, create table, clinical data, index, substr function, group by, sas function, data cleaning	source	14
87	Data Quality Scorecard	Tom Purvis	The approach in this paper shows users how to develop a data quality scorecard based on rules, and judged against a set of standards set by the user. Each rule (a/k/a metric) has a standard that determines whether it passes, fails, or needs review (a green, red, or blue score). A rule can be as simple as: Is the value for this column missing?, or is this column within a valid range? Further, it includes comparing a column to one or more other columns, or checking for specific invalid entries. It also includes rules that compare a column value to a lookup table to determine whether the value is in the lookup (a/k/a reference) table. Users can create their own rules and each column can have any number of rules. For example, a rule can be created to compare a dollar column to a range of acceptable values. The user can determine that it is expected that up to two percent of the values are allowed to be out of range	format statement, data cleaning, lookup, sample, dimension table, data mart, dataset, sas base, proc freq, proc step, macro variable, sas enterprise guide	source zip	16
88	Data Sampling Improvement by Developing the SMOTE Technique in SAS	Lina Maria Guzman Cartagena	A common problem when developing classification models is the imbalance of classes in the classification variable. This imbalance means that a cla ss is represented by a large number of cases while the other is represented by very few. When this happens, the predictive power of the developed model could be bias ed given that classification methods tend to favor the majority class since they are designed to minimize the error on the total data set regardless of the proportions or balance of the classes. Due to this problem, there are several techniques used to balance the distribution of the classification variable. One method is to reduce the si ze of t he majority class (Under sampling), another is to increase the number of cases in the minority class (Oversampling) or to use a combination of the two	sample, interval variable, roc curve, binary variable, nominal variable, proc freq, data cleaning	source	9
89	Data Summarization for a Dissertation: A Grad Student How-To Paper	Elisa L. Priest, Ashley Collinsworth	Graduate students often need to explore data and summarize multiple statistical models into tables for a dissertation. The challenges of data summarization include coding multiple, similar statistical models, and summarizing these models into meaningful tables for review. The default method is to type (or copy and paste) results into tables. This often takes longer than creating and running the analyses. Students might spend hours creating tables, only to have to start over when a change or correction in the underlying data requires the analyses to be updated. This paper gives graduate students the tools to efficiently summarize the results of statistical models in tables. These tools include a macro -based `SAS/STAT` analysis and `ODS` OUTPUT statement to summarize statistics into meaningful tables. Specifically, we summarize `PROC GLM` and `PROC LOGISTIC` output. We convert an analysis of hospital-acquired delirium from hundreds of pages of output into three formatted Microsoft `EXCEL` files	proc genmod, proc step, macro definition, proc logistic, ods output, dataset, estimate, output, proc glm, sas base, create table, macro call, macro facility, excel, logistic distribution	source	8
90	DATU - Data Automated Transfer Utility: A Program to Migrate Files from Mainframe to Windows	Jim Brittain, Robert Schwartz	to a Windows platform. This e-poster will highlight the Data Automated Transfer Utility (DATU) that was developed to migrate historic files between the two file systems using SAS macros and `SAS/CONNECT`. We will demonstrate how this program identifies the type of file, transfers the file appropriately, verifies the successful transfer, and provides the details in a Microsoft `EXCEL` report. `SAS/CONNECT` code, special system options, and mainframe code will be shown. In 2009 the CDC made the decision to retire a mainframe that had been used for years for primarily SAS analytic work and a few financial applications. The replacement platform for the SAS and analytic processing is a Windows based SAS Grid system referred to as the Consolidated Statistical Platform (CSP). The change from mainframe to Windows required the migration of approximately one hundred and eighty thousand files totaling approximately 16 terabytes	data cleaning, txt, macro variable, dataset, libname statement, sample, proc download, sas library, index, proc step	source	8
91	Defensive Coding by Example: Kick the Tires, Pump the Breaks, Check Your Blind Spots, and Merge Ahead!	Donna E. Levy, Nancy Brucken	As SAS programmers and statisticians, we rarely write programs that are run only once and then set aside. Instead, we are often asked to develop programs very early in a project, on immature data, following specifications that may be little more than a guess as to what the data is supposed to look like. These programs will then be run repeatedly on periodically updated data through the duration of the project. This paper offers strategies for not only making those programs more flexible, so they can handle some of the more commonly encountered variations in that data, but also for setting traps to identify unexpected data points that require further investigation. We will also touch upon some good programming practices that can benefit both the original programmer and others who might have to touch the code	data cleaning, batch job, raw data, missing value, proc step, drop statement, if-then-else, macro variable, best response	source	17
92	Defining and Mapping a Reasonable Distance for Consumer Access to Market Locations	Sarah Campbell	We use a series of SAS data steps and SQL joins to calculate reasonable distance thresholds useful for exploring consumer access to bank branches. We demonstrate the calculations using geocoded addresses from the FDIC Summary of Deposits data and Census geospatial data, including point shapefiles containing population-weighted centroids of census tracts. We start with a `PROC SQL` step to employ a Cartesian join on bank branch location data with the dataset containing population-weighted centroid coordinates. Using the GEODIST function in SAS, we are able to calculate the distance to the nearest bank branch from the population-weighted centroid of each census tract. The tract dataset is then grouped - by Metropolitan Statistical Area (MSA), Metropolitan Division (MD), or non-metropolitan (non-MSA) area for each state - and sorted in ascending order within each grouping by distance to the nearest bank branch using the RETAIN function	proc sql, where statement, cross join, proc step	source	5
93	Demystifying Date and Time Intervals	Derek P. Morgan	Intervals have been a feature of base SAS for a long time, allowing SAS users to work with commonly (and not-so- commonly) defined periods of time such as years, months, and quarters. With the release of SAS 9, there are more options and capabilities for intervals and their functions. This paper will first discuss the basics of intervals in detail, and then we will discuss several of the enhancements to the interval feature, such as the ability to select how the INTCK() function defines interval boundaries and the ability to create your own custom intervals beyond multipliers and shift operators	sysdate9, missing value, elapsed time, sample, date variable, time series analysis	source	13
94	Deriving Adverse Event Records Based on Study Treatments	Jonson Jiang	This paper puts forward an appr oach on how to create duplicate records from one Adverse Event (AE) datum based on study treatments. In order to fulfill this task, one flag was created to check if we need to produce duplicate records depending on an existing AE in different treatment periods. If y es, then create these duplicate records and derive AE dates for these duplicate records based on treatment periods or discontinued dates	treatment period, adverse events	source	6
95	Designing Big Data Analytics Undergraduate and Postgraduate Programs for Employability by Using National Skills Frameworks	Richard Self	There is a widely forecast skills gap developing between the numbers of BDA graduates and the predicted jobs market. Many universities are developing innovative programmes to increase the numbers of BDA graduates and postgraduates. The University of Derby has recently developed two new programmes which aim to be unique and to offer the applicants highly attractive and career enhancing programmes of study. One programme is an undergraduate Joint Honours programme which pairs Analytics with a range of alternative subject areas; the other is a Masters programme which has specific emphasis on Governance and Ethics. A critical aspect of both programmes is the synthesis of a Personal Development Planning Framework that aids the students to evaluate their current status, identify the steps needed to develop towards their career goals and to also to provide a means of recording their achievements with evidence that can then be used in job applications	sas base, big data	source	5
96	Determining the Key Success Factors for Hit Songs in the Billboard Music Charts	Piboon Banpotsakun, Jongsawas Chongwatpol	Analyzing the key success factors for hit songs in the billboard music chart is an ongoing area of interest to the music industry. Although there have been many studies on predicting whether a song has the potential to become a hit song over the past decad es, the following research question remains - Can hit songs be predicted? And if yes, what are the characteristics of those hit songs? This study applies data mining techniques using SAS Enterprise Miner TM to understand why some music is more popular tha n the other. In particular, certain songs are considered 'one -hit-wonders', which are in the top billboard chart only once; meanwhile the other are acknowledge masterpieces. With 2,139 data records, the	proc hpsplit, neural network, regression analysis, type ii error, polynomial regression, response variable, %if-%then-%else, data mining	source	12
97	Did Your Join Work Properly?	Emmy V Pahmer	We have many options for performing merges or joins these days, each with various advantages and disadvantages. Depending on how you perform your joins, different checks can help you verify whether the join was successful. In this presentation, we look at some sample data, use different methods, and see what kinds of tests can be done to ensure that the results are correct. If the join is performed with `PROC SQL` and two criteria are fulfilled (the number of observations in the primary data set has not changed [presuming a one-to- one or many-to-one situation], and a variable that should be populated is not missing), then the merge was successful	macro variable, proc sql	source	5
98	Discovering Personality Type through Text Mining in SAS Enterprise Miner 12.3	Mark L. Schneider, Andrew Van Der Werff	Presentation	data mining, proc hpsplit	source	7
99	Divide and Conquer - Writing Parallel SAS Code to Speed Up Your SAS Program	Doug Haigh	Being able to split SAS processing over multiple SAS processers on a single machine or over multiple machines running SAS, as in the case of SAS Grid Manager, enables you to get more done in less time. This paper looks at the methods of using `SAS/CONNECT` to process SAS code in parallel, including the SAS statements, macros, and PROCs available to make this processing easier for the SAS programmer. SAS products that automatically generate parallel code are also highlighted	macro variable, sample, sas grid, parallel processing, cpu time, proc step, macro put, macro definition	source zip	13
100	Documentation as You Go: aka Dropping Breadcrumbs!	Elizabeth Axelrod	Presentation	raw data, sas log	source	4
101	Does Class Rank Align with Future Potential?	Jonathan Gomez Martinez, Christopher Simpson	Presentation	proc sql, proc step	source zip	3
102	Don't Be a Litterbug: Best Practices for Using Temporary Files in SAS	Richard D. Langston	This paper explains best practices for using temporary files in SAS programs. These practices include using the TEMP access method, writing to the WORK directory, and ensuring that you leave no litter files behind. An additional special temporary file technique is described for mainframe users	macro variable, macro definition, filename statement, proc step	source	5
103	Don't Copy and Paste--Use BY Statement Processing with ODS to Make Your Summary Tables	Jeffrey M. Gossett, Mallikarjuna Rettiganti	A majority of the manuscripts in medical journals contain summary tables that combine simple summaries and between-group comparisons. These tables typically combine summary statistics for categorical and continuous variables into one table. The `FREQ` procedure is generally used to summarize categorical variables and compare percentages between groups using a Chi-square or a Fisher's exact test. For continuous variables, the `MEANS` procedure is used to summarize data as either means and standard deviation or medians and quartiles. These are then compared between groups by using the GLM (or TTEST) or `NPAR1WAY` procedure, depending on whether one is interested in a parametric test or a non-parametric test. The outputs from these different procedures are then combined and presented in a concise format ready for publication. Currently there is no straightforward way in SAS to build these tables in a presentable format that can then be customized to individual tastes	nominal variable, interval variable, proc step, class variable, chi-square test, proc print, proc transpose, by group processing, wilcoxon test, excel	source	18
104	Donor Sentiment and Characteristic Analysis Using SAS Enterprise Miner and SAS Sentiment Analysis Studio	Ramcharan Kakarla, Goutam Chakraborty	It has always been a million-dollar question, "What inhibits a donor to donate?" Many successful universities have deep roots in annual giving. We know sentiment is a key factor in drawing attention to engage donors. This paper is a summary of findings about donor behaviors using textual analysis combined with the power of predictive modeling. In addition to identifying the characteristics of general donors, the paper focuses on identifying the characteristics of a first-time donor. It distinguishes the features of the first-time donor from the general donor pattern. A data set containing 247,000 records was obtained from a University Foundation alumni database, Facebook, and Twitter. Solicitation content such as email subject lines sent to the prospect base was considered. Time-dependent data and time-independent data were categorized to make unbiased predictions about the first-time donor	sample, data mining, unstructured data, data cleaning, proc hpsplit, likelihood function, regression analysis	source	12
105	Double Generalized Linear Models Using SAS: The %DOUBLEGLM Macro	Paulo Silva, Alan Ricardo da Silva	The purpose of this paper is showing a SAS macro named %doubleglm in order that users can model the mean and dispersion jointly using double generalized linear models described in Nelder and Lee (1991, 1998). The R functions fitjoint and dglm (R Development Core Team, 2011) were used to verify the suitability of the %doubleglm macro estimates. The results showed that estimates generated %doubleglm macro by are closer than R functions	macro loop, likelihood function, response variable, proc genmod, estimate, proc calis, maximum likelihood estimation, proc model, gamma distribution, sample weight, poisson distribution, sample, binomial distribution	source zip	16
106	Drilling for Deepwater Data: A Forensic Analysis of the Gulf of Mexico Deepwater Horizon Disaster	Jim Duarte, Keith R. Holdaway, Moray Laing	During the cementing and pumps-off phase of oil drilling, drilling operations need to know, in real time, about any loss of hydrostatic or mechanical well integrity. This phase involves not only big data but also high-velocity data. Today's state-of-the-art drilling rigs have tens of thousands of sensors. These sensors and their data output must be correlated and analyzed in real time. This paper shows you how to leverage SAS Asset Performance Analytics and SAS Enterprise Miner to build a model for drilling and well control anomalies, to fingerprint key well control measures of the transient fluid properties , and to operationalize these analytics on the drilling assets with SAS Event Stream Processing. We cover the implementation and results from the Deepwater Horizon case study, demonstrating how SAS analytics enables the rapid differentiation between safe and unsafe modes of operation	data mining, stream processing, real-time processing, sas enterprise guide	source	8
107	Driving SAS with Lua	Paul Tomas	Programming SAS has just been made easier, now that SAS 9.4 has incorporated the Lua programming language into the heart of the SAS System. With its elegant syntax, modern design, and support for data structures, Lua offers you a fresh way to write SAS programs, getting you past many of the limitations of the SAS macro language. This paper shows you how you can get started using Lua to drive SAS, via a quick introduction to Lua and a tour through some of the features of the Lua and SAS combination that make SAS programming easier. SAS macro programming is also compared with Lua, so that you can decide where you might benefit most from using each language	proc step, proc lua, macro facility, macro variable, sas function, macro definition, missing value, proc print, proc fcmp, proc sort	source	16
108	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 a dditional 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 with 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, macro variable, sas base, proc fcmp, sas function, sas programmer, proc sql, parallel processing, return, sysdate9, macro definition, temporary array, market basket analysis, dataset	source	32
109	Dynamic Dashboards Using SAS	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	key performance indicator, business intelligence	source	10
110	Dynamic Decision-Making Web Services Using SAS Stored Processes and SAS Business Rules Manager	Lori Small, Chris Upton	With the latest release of SAS Business Rules Manager, decision-making using SAS Stored Processes is now easier with simplified deployment via a web service for integration with your applications and business processes. This paper shows you how a user can publish analytics and rules as SOAP-based web services, track its usage, and dynamically update these decisions using SAS Business Rules Manager. In addition, we demonstrate how to integrate with SAS Model Manager using SAS Workflow to demonstrate how your other SAS applications and solutions can also simplify real - time decision-making through business rules	web service, data validation rules, business intelligence, sas model manager, proc step, symbol table, data cleaning	source	16
111	E-Z Simple Hash Object Lookups	Andrew Dagis	We have to pull data from several data files in creating our working databases. The simplest use of SAS hash objects greatly reduces the time required to draw data from many sources when compared to the use of multiple `PROC` sorts and merges	hash table, data step, proc sql, proc step, lookup, sas 9, temporary array, sysrc, dataset, temporary dataset	source zip	6
112	Easing into Analytics Using SAS Enterprise Guide 6.1	Marje Fecht	Do you need to deliver business insight and analytics to support decision-making? Using SAS Enterprise Guide, you can access the full power of SAS for analytics, without needing to learn the details of SAS programming. This presentation focuses on the following uses of SAS Enterprise Guide: Exploring and understanding-getting a feel for your data and for its issues and anomalies Visualizing-looking at the relationships, trends, surprises Consolidating-starting to piece together the story Presenting-building the insight and analytics into a presentation using SAS Enterprise Guide	sas enterprise guide, scatter plot, proc freq	source	11
113	Edit the Editor: Creating Keyboard Macros in SAS Enterprise Guide	Christopher J. Bost	Programmers can create keyboard macros to perform common editing tasks in SAS Enterprise Guide. This paper introduces how to record keystrokes, save a keyboard macro, edit the commands, and assign a shortcut key. Sample keyboard macros are included. Techniques to share keyboard macros are also covered	sas enterprise guide, sample	source zip	7
114	Educating Future Business Leaders in the Era of Big Data	Tonya E. Balan	At NC State University, our motto is "Think and Do." When it comes to educating students in the Poole College of Management, that means that we want them to not only learn to think critically but also to gain hands-on experience with the tools that will enable them to be successful in their careers. And, in the era of big data, we want to ensure that our students develop skills that will help them to think analytically in order to use data to drive business decisions. One method that lends itself well to "thinking and doing" is the case study approach. In this paper, we will discuss the case study approach for teaching analytical skills and highlight the use of SAS software for providing practical, hands-on experience with manipulating and analyzing data. The approach will be illustrated with examples from specific case studies that have been used for teaching introductory and intermediate courses in business analytics	big data, data mining, proc hpsplit, data science, proc freq, box plot	source	8
115	Efficient Ways to Build Up Chains and Waves for Respondent-Driven Sampling Data	Wen Song	Presentation	sas base, sample	source	3
116	Efficiently Using SAS Data Views	Shigui Weng, Shy Degrace, Ya Jiun Tsai	For the Research Data Centers of the United States Census Bureau, the demand for disk space substantially increases with each passing year. Meanwhile, providing data access to our researchers in a reliable, efficient, and timely manner is a great challenge for us. In addition, more demands from researchers and data providers will come in the near future as the Research Data Centers have just expanded to the Federal Statistical Research Data Centers to provide data access to researchers not just for the Census Bureau but also for multiple government agencies. SAS data views provide an alternative way to deploy data sets in our data repository. Efficiently using the SAS data view might successfully address these challenges within the Research Data Centers. This paper discusses the usage and benefits of SAS data views, and study the disk space and time efficiency of using SAS `DATA STEP` views with regular ASCII files, compressed ASCII files, and compressed SAS data sets	cpu time, dataset, view, proc step, proc sql, filename statement, proc freq, proc means, access control, proc sort	source	16
117	Encore: Introduction to Bayesian Analysis Using SAS/STAT	Maura E. Stokes	The use of Bayesian methods has become increasingly popular in modern statistical analysis, with applica- tions in numerous scientific fields. In recent releases, SAS has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures as well as the `MCMC` procedure, which is designed for general Bayesian modeling. This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then describes the built-in Bayesian capabilities provided in `SAS/STAT`, which became available for all platforms with `SAS/STAT` 9.3, with examples from the GENMOD and PHREG procedures. How to specify prior distributions, evaluate convergence diagnostics, and interpret the posterior summary statistics is discussed. Foundations Bayesian methods have become a staple for the practicing statistician	bayesian statistics	source	25
118	Enhancing Infrastructure for Growth	Amber Randall	Presentation	pharmaceutical industry, clinical programming	source	8
119	Entity Resolution and Master Data Life Cycle Management in the Era of Big Data	John Talburt	Proper management of master data is a critical component of any enterprise information system. However, effective master data management (MDM) requires that both IT and Business understand the life cycle of master data and the fundamental principles of entity resolution (ER). This paper provides a high-level overview of current practices in data matching, record linking, and entity information life cycle management that are foundational to building an effective strategy to improve data integration and MDM. These include the need for ongoing ER analytics that systematically and quantitatively measure ER performance, investing in clerical review and asserted resolution for continuous improvement , and addressing the large-scale ER challenge through distributed processing	type ii error, type i error, distributed processing, entity resolution, master data management, sample	source	16
120	Entropy-Based Measures of Weight of Evidence and Information Value for Variable Reduction and Segmentation for Continuous Dependent Variables	Alec Zhixiao Lin	Weight of Evidence (WOE) and Information Value (IV) have become important tools for analyzing and modeling binary outcomes such as default in payment, response to a marketing campaign, etc. This application encounters difficulty when dealing with continuous outcomes because non-occurrence is either unquantifiable or non-existent. Going back to the fundamentals of Information Theory, this paper suggests a set of alternative formulae that attempts to dichotomize high occurrences and low occurrences in order to expand the use of WOE and IV to continuous outcomes such as sales volume, loss amount, etc. A SAS macro program is provided that will efficiently evaluate the predictive power of continuous, ordinal and categorical variables together and yield useful suggestions for variable reduction/selection, segmentation and subsequent linear or logistic regressions	numeric variable, ordinal variable, proc step, nominal variable, proc varreduce, predictor variable, regression analysis, sample, proc sql, response variable	source	27
121	Equivalence and Noninferiority Testing Using SAS/STAT Software	John M. Castelloe, Donna Watts	Proving difference is the point of most statistical testing. In contrast, the point of equivalence and noninferiority tests is to prove that results are substantially the same, or at least not appreciably worse. An equivalence test can show that a new treatment, one that is less expensive or causes fewer side effects, can replace a standard treatment. A noninferiority test can show that a faster manufacturing process creates no more product defects or industrial waste than the standard process. This paper reviews familiar and new methods for planning and analyzing equivalence and noninferiority studies in the POWER, TTEST, and FREQ procedures in `SAS/STAT` software	sample, proc step, proc power, proc ttest, confidence interval, equivalence testing, non-inferiority, sample size, proc freq, relative risk, likelihood function, binomial distribution	source	23
122	Establishing a Health Analytics Framework	Krisa Tailor, Jeremy Racine	As the big data wave continues to magnify in the healthcare industry, health data available to organizations are becoming richer and more diverse. Whether it is financial, clinical, or mobile health data, the sheer amount of data surfacing across payer and provider groups alike is remarkable. In fact, McKinsey & Company predicts that the value of big data in healthcare could reach $300 billion by the year 2020. Through the midst of this growth, and the continued pressure to improve the quality of care while reducing costs, healthcare organizations are grappling to make sense of the immense amount of information gener ated each day. As information technology tools become increasingly powerful, the opportunities to operationalize analytics to better understand this data and improve the practice of medicine are becoming greater and richer	insurance claims, clinical data	source	10
123	Examination of Three SAS Tools for Solving a Complicated Data Summary Problem	Carry W. Croghan	When faced with a difficult data analysis problem, a SAS programmer has many options to solve the problem. In this paper, three different approaches are evaluated and compared in terms of effectiveness, efficiency, debugging, and clarity of the code. The three methods include linearizing the data, using SQL Cartesian joins, and using sequential data processing. Inconsistencies in the raw data caused the data linearization to be problematic. The output generated by a multi-stage, air monitoring instrument that collected several measurements every 5 minutes for 9 years resulted in more than 800,000 records. The large number of records and the need for many-to-many merges resulted in a long run time for the SQL code. The sequential data processing, although older technology, provided the most t ime efficient and error-free results	proc sql, lag function, create table, proc step, cross join, retain, sample	source	12
124	Experimental Approaches to Marketing and Pricing Research	Max Friedauer, Jason Greenfield, Yuhan Jia, Joseph Thurman	Design of Experiments (DOE) is an essential component of laboratory, greenhouse and field research in the natural sciences. It has also been an integral part of scientific enquiry in such diverse social science fields such as education, psychology, marketing, pricing and social works. The principle and practices of DOE are among the oldest and the most advanced tools within the realm of statistics. DOE classification schemes, however, are diverse and at times confusing. In this presentation we will provide a simple conceptual classification framework in which experimental methods are grouped into classical and statistical approaches. The classical approach is further divided into pre-, quasi- and true-experiments. The statistical approach is also divided into one-, two-, and more than two factor experiments. Within these broad categories, we will review several contemporary and widely used designs and their applications	randomization, anova, predictor variable, degrees of freedom, response variable, marketing mix, proc freq	source	22
125	Exploring Number Theory Using Base SAS	Matthew Duchnowski	Long before analysts began mining large datasets, mathematicians sought truths hidden within the set of natural numbers. This exploration was formalized into the mathematical subfield known as Number Theory. Though this discipline has proven useful for many applied fields, Number Theory delights in numerical truth for its own sake. The austere and abstract beauty of Number Theory prompted nineteenth century mathematician Carl Friedrich Gauss to dub it the "Queen of Mathematics." Here, the author will show that the analytical power of the SAS engine is well-suited to the exploration of topics in Number Theory. While the concepts discussed here comprise only a small survey of topics, readers can imitate the general approach and craft their own investigations	proc step, sas base	source	14
126	Extending the Scope of Custom Transformations	Emre Saricicek, Dean Huff	Building and maintaining a data warehouse can require complex series of jobs. Having an ETL flow that is reliable and well integrated is one big challenge. An ETL process might need some pre- and post- processing operations on the database to be well integrated and reliable. Some might handle this via maintenance windows. Others like us might generate custom transformations to be included in SAS Data Integration Studio jobs. Custom transformations in SAS Data Integration Studio can be used to speed ETL process flows and reduce the database administrator's intervention after ETL flows are complete. In this paper, we demonstrate the use of custom transformations in SAS Data Integration Studio jobs to handle database-specific tasks for improving process efficiency and reliability in ETL flows	sas data integration studio, record count, proc sql	source	8
127	Federated Security Domains with SAS and SAML	Mike Roda	From large holding companies with multiple subsidiaries to loosely affiliated state educational institutions, security domains are being federated to enable users from one domain to access applications in other domains and ultimately save money on software costs through sharing. Rather than rely on centralized security, applications must accept claims-based authentication from trusted authorities and support open standards such as Security Assertion Markup Language (SAML) instead of proprietary security protocols. This paper introduces SAML 2.0 and explains how the open source SAML implementation known as Shibboleth can be integrated with the SAS 9.4 security architecture to support SAML. It then describes in detail how to set up Microsoft Active Directory Federation Services (AD FS) as the SAML identity provider, how to set up the SAS middle tier as the relying party, and how to troubleshoot prob lems	service provider, attrib statement	source	14
128	Feeling Anxious about Transitioning from Desktop to Server? Key Considerations to Diminish Your Administrators' and Users' Jitters	Kate Schwarz, Donna Bennett, Margaret A. Crevar	As organizations strive to do more with fewer resources, many modernize their disparate PC operations to centralized server deployments. Administrators and users share many concerns about using SAS on a Microsoft Windows server. This paper outlines key guidelines, plus architecture and performance considerations, that are essential to making a successful transition from PC to server. This paper outlines the five key considerations for SAS customers who will change their configuration from PC -based SAS to using SAS on a Windows server: 1) Data and directory references; 2) Interactive and surrounding applications; 3) Usability; 4) Performance; 5) SAS Metadata Server	sas enterprise guide, sas base, sas studio, proc step, libname statement, excel	source	13
129	Filling your SAS Efficiency Toolbox: Creating a Stored Process to Interact with Your Shared SAS Server Using the X and SYSTASK Commands	Cody Murray, Chad Stegeman	Presentation	stored procedure, sas enterprise guide	source	8
130	Find What You Are Looking For And More in SAS Enterprise Guide	Casey Smith	Are you looking to track changes to your SAS programs? Do you wish you could easily find errors, warnings, and notes in your SAS logs? Looking for a convenient way to find point -and-click tasks? Want to search your SAS Enterprise Guide project? How about a point-and-click way to view SAS system options and SAS macro variables? Or perhaps you want to upload data to the SAS LASR Analytics Server, view SAS Visual Analytics reports, or run SAS Studio tasks, all from within SAS Enterprise Guide? You can find these capabilities and more in SAS Enterprise Guide. Knowing what tools are at your disposal and how to use them will put you a step ahead of the rest. Come learn about some of the newer features in SAS Enterprise Guide 7.1 and how you can leverage them in your work	sas enterprise guide, macro variable, macro definition, sas studio, sql pass-through, sas enterprise guide project, macro function, options statement, implicit sql pass-through, sas programmer	source	19
131	Fixing Lowercase Acronyms Left Over from the PROPCASE Function	Joe DeShon	The PROPCASE function is useful when cleansing a database of names and addresses in preparation for mailing. But it doesn't know the difference between a proper name (in which proper case should be used) and an acronym (which should be left all upper case). This paper describes an algorithm that determines with reasonable accuracy if a word is an acronym and converts it to upper case if it is	upcase function, character functions, substr function, if-then-else, macro variable, macro definition	source zip	6
132	Forecasting Vehicle Sharing Demand Using SAS Forecast Studio	Kushal Kathed, Goutam Chakraborty, Ayush Priyadarshi	As pollution and population continue to increase, new concepts of ecofriendly commutating evolve. One of the emerging concepts is the bicycle sharing system; it is a bike rental service on a short -term basis at a moderate price. It provides people the flex ibility to rent a bike from one location and return it to another location. This business is quickly gaining popularity all over the globe. In May 2011, there were only 375 bike rental schemes comprised of nearly 236,000 bikes in USA . However, this number jumped to 535 bike sharing programs with approximately 517,000 bikes in just a couple of years. It is expected that this trend will continue to grow at a similar pace in the future. Most of the businesses involved in this system of bike rental are faced w ith the challenge of balancing between supply and inconsistent demand	time series analysis, scenario analysis, outlier, response variable, predictor variable	source	9
133	Forest Plotting Analysis Macro %FORESTPLOT	Jeffrey P. Meyers, Qian Shi	The forest plot is a powerful and versatile tool for visually presenting model estimates for multivariable analysis, or illustrating association measures of key interested factor across various models or subgroup analyses. The ability to see if two values are significantly different from each other, or if a covariate has a significant meaning on its own when compared to a reference value, is made much simpler in a forest plot rather than sifting through numbers in a report table. The amount of data preparation in order to build a high quality forest plot in SAS can be tremendous as the programmer will need to run analyses, extract the estimates to be plots, and structure the estimates in a format conducive to generating a forest plot. This code required for this process is often replicated repeatedly for multiple models, which is inefficient and prone to errors	forest plot, ods output, output, logistic distribution, logistic regression, regression analysis, odds ratio, binomial distribution, proc phreg, graph template language, confidence interval, hazard ratio, numeric variable, proc means, macro parameter, character variable, proc genmod, proc calis, proc logistic, subgroup analysis, proc sql, likelihood function, nominal variable, macro loop	source	18
134	Frequently Asked Questions Regarding Storage Configurations	Tony Brown, Margaret A. Crevar	The SAS Global Forum paper "Best Practices for Configuring Your I/O Subsystem for SAS 9 Applications" provides general guidelines for configuring I/O subsystems for your SAS applications. The paper reflects updated storage and virtualization technology. This companion paper (" Frequently Asked Questions Regarding Storage Configurations") is commensurately updated, including new storage technologies such as storage virtualization, storage tiers (including automated tier management), and flash storage. The subject matter is voluminous, so a frequently asked questions (FAQ) format is used. Our goal is to continually update this paper as additional field needs arise and technology dictates	dataset, business intelligence	source	10
135	From a One-Horse to a One-Stoplight Town: A Base SAS Solution to Preventing Data Access Collisions Using Shared and Exclusive File Locks	Troy Martin Hughes	Data access collisions occur when two or more processes attempt to gain concurrent access to a single data set, and represent a common obstacle to SAS practitioners in multi-user environments. As SAS instances expand to infrastructures and ultimately empires, the inherent increased complexities must be matched with commensurately higher code quality standardsi. Moreover, permanent data sets will attract increasingly more devoted users and automated processes clamoring for attention. As these dependencies increase, so too does the likelihood of access collisions that, if unchecked or unmitigated, leads to certain process failure. The `SAS/SHARE` moduleii offers concurrent file access capabilities, but causes a (sometimes dramatic) reduction in processing speed, must be licensed and purchased separately from Base SAS, and is not a viable solution for many organizations	sas base, sysrc, exception handling, proc sort, macro variable, macro function, dataset, macro definition, macro put, likelihood function, parallel processing, sas library, proc step, automatic macro variable, permanent library	source	18
136	From Backpacks to Briefcases: Making the Transition from Grad School to a Paying Job	Lauren Hall, Elisa L. Priest	During grad school, students learn SAS in class or on their own for a research project. Time is limited, so faculty have to focus on what they know are the fundamental skills that students need to successfully complete their coursework. However, real-world research projects are often multifaceted and require a variety of SAS skills. When students transition from grad school to a paying job, they might find that in order to be successful, they need more than the basic SAS skills that they learned i n class. This paper highlights 10 insights that I've had over the past year during my transition from grad school to a paying SAS research job. I hope this paper will help other students make a successful transition. Top 10 insights: 1. You still get graded, but there is no syllabus. 2. There isn't time for perfection. 3. Learn to use your resources. 4. There is more than one solution to every problem. 5. Asking for help is not a weakness. 6. Working with a team is required. 7	proc step, sas programming, proc means, proc sql	source	6
137	From ETL to ETL VCM: Ensure the Success of a Data Migration Project through a Flexible Data Quality Framework Using SAS Data Management	Yves Wouters, Valérie Witters	Data Quality is now more important than ever before. According to Gartner (2011), poor data quality is the primary reason why 40% of all business initiatives fail to achieve their targeted benefits. To anticipate the ever growing importance of Data Quality and ensure the success of a business initiative, Deloitte Belgium has created an end-to-end Data Quality framework using SAS DataFlux Data Management to rapidly identify and resolve root causes of data quality issues to jumpstart business initiatives such as a data migration. Moreover, the framework uses both standard SAS DataFlux Data Management functionalities (such as standardization, parsing, etc.) as well as advanced features (such as using macro's, dynamic profiling in deployment mode, extracting the profiling results, etc.) allowing the framework to be agile and flexible as well as maximize the re-usability of specific components built in SAS DataFlux Data Management	macro variable, data cleaning, sql server, proc sql	source	10
138	Functional Modeling of Longitudinal Data with the SSM Procedure	Rajesh S. Selukar	In many studies, a continuous response variable is repeatedly measured over time on one or more subjects. The subjects might be grouped into different categories, such as cases and controls. The study of resulting observation profiles as functions of time is called functional data analysis. This paper shows how you can use the `SSM` procedure in `SAS/ETS` software to model these functional data by using structural state space models (SSMs). A structural SSM decomposes a subject profile into latent components such as the group mean curve, the subject-specific deviation curve, and the covariate effects. The `SSM` procedure enables you to fit a rich class of structural SSMs, which permit latent components that have a wide variety of patterns. For example, the latent components can be different types of smoothing splines, including polynomial smoothing splines of any order and all L-splines up to order 2	proc step, proc mixed, binary variable	source zip	19
139	Fuzzy Matching	Arti Virkud	Quality measurement is increasingly important in the health-care sphere for both performance optimization and reimbursement. Treatment of chronic conditions is a key area of quality measurement. However, medication compendiums change frequently, and health-care providers often "free text" medications into a patient's record. Manually reviewing a complete medications database is time consuming. In order to build a robust medications list, we matched a pharmacist-generated list of categorized medications to a raw medications database that contained names, name- dose combinations, and misspellings. The matching procedure used is called COMPGED. We were able to combine a truncation function and an upcase function to optimize the output of COMPGED. Using these combinations and manipulating the scoring metric of COMPGED enabled us to narrow the database list to medications that were relevant to our categories	proc sql, proc step	source	3
140	Garbage In, Gourmet Out: How to Leverage the Power of the SAS Quality Knowledge Base	Brian Rineer	Companies spend vast amounts of resources developing and enhancing proprietary software to clean their business data. Save time and obtain more accurate results by leveraging the SAS Quality Knowledge Base (QKB), formerly a DataFlux Data Quality technology. Tap into the existing QKB rules for cleansing	data cleaning, data governance	source	15
141	GEN_OMEGA2: A SAS Macro for Computing the Generalized Omega-Squared Effect Size Associated with Analysis of Variance Models	Anh P. Kellermann, Yi-Hsin Chen, Jeffrey D. Kromrey, Thanh Pham, Patrice Rasmussen, Patricia Rodríguez de Gil, Jeanine L. Romano	Effect sizes are strongly encouraged to be reported in addition to statistical significance and should be considered in evaluating the results of a study. The choice of an effect size for ANOVA models can be confusing because indices may differ depending o n the research design as well as the magnitude of the effect. Olejnik and Algina (2003) proposed the generalized eta -squared and omega -squared effect sizes which are comparable across a wide variety of research designs. This paper provides a SAS macro for computing the generalized omega -squared effect size associated with analysis of variance models by utilizing data from `PROC GLM` `ODS` tables. The paper provides the macro programming language, as well as results from an executed example of the macro. Keywords: Effect sizes, ANOVA, Generalized Eta -Squared, Generalized Omega -Squared, BASE SAS,	effect size, anova, main effect, proc glm, proc step, sample, predictor variable, proc genmod, response variable, macro definition, dataset, data step, proc anova	source	12
142	Getting Started with Data Governance	Gregory S. Barnes Nelson, Lisa Dodson	While there has been tremendous progress in technologies related to data storage, high performance computing and advanced analytic techniques, organizations have only recently begun to comprehend the importance of parallel strategies that help manage the cacophony of concerns around access, quality, provenance, data sharing and use. While data governance is not new, the drumbeat around this, along with master data management and data quality, is approaching a crescendo. Intensified by the increase in consumption of information, expectations about ubiquitous access, and highly dynamic visualizations, these factors are also circumscribed by security and regulatory constraints. In this paper we provide a summary of what data governance is and its importance, which is, as you would expect. However, we go beyond the obvious and provide practical guidance on what it takes to build out a data governance capability appropriate to the scale, size and purpose of the organization and its culture	data governance, master data management	source	15
143	Getting Your Hands on Reproducible Graphs	Rebecca Ottesen, Leanne Goldstein	Learning the SAS Graph Template Language (GTL) may seem like a daunting task. However, creating customized graphics with SAS is quite easy using many of the tools offered with Base SAS software. The point-and-click interface of `ODS` Graphics Designer provides us with a tool that can be used to generate highly polished graphics and to store the GTL-based code that creates them. This opens the door for users who would like to create canned graphics that can be used on various data sources, variables, and variable types. In this hands-on training, we explore the use of `ODS` Graphics Designer to create sophisticated graphics and save the template code. We then discuss modifications using basic SAS macros in order to create stored graphics code that is flexible enough to accommodate a wide variety of situations	proc step, macro variable, scatter plot, bar chart, histogram, forest plot, proc template, box plot, proc univariate, regression analysis, odds ratio, macro definition, proc means, logistic distribution, confidence interval, numeric variable, proc freq, where statement, nominal variable, interval variable, class variable, macro call, estimate, kaplan-meier plot, data science	source	19
144	Getting Your SAS Program to do Your Typing for You!	Nancy K. Wilson	Do you have a SAS program that requires adding file names to the input every time you run it? Aren't you tired of having to check for the files, check the names and type them in? Check out how this SAS Enterprise Guide Project checks for files, figures out the file names and eliminates the need for having to type in the file names for the input data files!	proc step, call execute, proc contents, txt	source zip	6
145	Graphing Made Easy with SGPLOT and SGPANEL Procedures	Susan J. Slaughter	When `ODS` Graphics was introduced a few years ago, it gave SAS users an array of new ways to generate graphs. One of those ways is with Statistical Graphics procedures. Now, with just a few lines of simple code, you can create a wide variety of high-quality graphs. This paper shows how to produce single-celled graphs using `PROC SGPLOT` and paneled graphs using `PROC SGPANEL`. This paper also shows how to send your graphs to different `ODS` destinations, how to apply `ODS` styles to your graphs, and how to specify properties of graphs, such as format, name, height, and width	proc sgpanel, bar chart, series plot, proc step, proc sgplot, histogram, ods html	source	19
146	Graphs Are Easy with SAS 9.4	Sanjay N. Matange	Axis tables, polygon plot, text plot, and more features have been added to Statistical Graphics ( SG) procedures and Graph Template Language (GTL) for SAS 9.4. These additions are a direct result of your feedback and are designed to make creating graphs easier. Axis tables let you add multiple tables of data to your graphs and to correctly align with the axis values with the right colors for group values in your data. Text plots can have rotated and aligned text anywhere in the graph. You can overlay jittered markers on box plots, use images and font glyphs as markers, specify group attributes without making style changes, and create entirely new custom graphs using the polygon plot. All this without using the annotation facility, which is now supported both for SG procedures and GTL. This paper guides you through these exciting new features now available in SG procedures and GTL	bar chart, scatter plot, histogram, box plot, forest plot, proc sgplot, series plot, kaplan-meier plot	source zip	15
147	Great Performances: SAS Visual Analytics Performance Monitoring and Enhancement	Jonathan Pletzke	At the University of North Carolina at Chapel Hill, we had the pleasure of rolling out an enterprise-wide SAS Visual Analytics environment in 10 months, with strong support from SAS. We encountered many bumps in the road, moments of both mountain highs and worrisome lows as we learned what we could and could not do, and new ways to accomplish our goals. Our journey started in December of 2013 when a decision was made to try SAS Visual Analytics for all reporting, and only incorporate other solutions if and when we hit an insurmountable obstacle. We are still strongly using SAS Visual Analytics and are augmenting the tools with additional products. Along the way, we learned a number of things about the SAS Visual Analytics environment that are gems, whether one is relatively new to SAS or an old hand. Measuring what is happening is paramount to knowing what constraints exist in the system before trying to enhance performance. Targeted improvements will help if measurements can be made before and after each alteration. There are a few architectural alterations that can help in general, but we've seen that measuring is the certain way to know the problems and then whether the cures were effective	load, relational database	source	16
148	Gross Margin Percent Prediction: Using the Power of SAS Enterprise Miner 12.3 to Predict the Gross Margin Percent for a Steel Manufacturing Company	Kushal Kathed, Patti Jordan, Ayush Priyadarshi	Presentation	data mining, response variable, data cleaning, correlation	source	3
149	Hands-Off SAS Administration - Using Batch Tools to Make Your Life Easier	Eric Bourn, Amy Peters, Bryan Wolfe	As a SAS Intelligence Platform Administrator, have your eyes ever glazed over as you performed repetitive tasks in SAS Management Console or some other administrative user interface? Perhaps you're setting up metadata for a new department, managing a set of backups, or promoting content between dev, test, and prod environments. Did you know there is a large library of batch utilities to help you automate many of these common administration tasks? This paper explores content reporting and management utilities, such as viewing authorizations or relationships between content, as well as administrative tasks such as analyzing, creating, or deleting metadata repositories or performing a backup of the system. The batch utilities can be incorporated into scripts so that you can run them repeatedly on either an ad hoc or scheduled basis. Give your mouse a rest and save yourself some time	access control, spk file, sas management console	source	12
150	Hands-On SAS Macro Programming Essentials for New Users	Kirk Paul Lafler	The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This hands-on workshop teaches essential macro coding concepts, techniques, tips and tricks to help beginning users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees learn how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct simple arithmetic and logical expressions; interface the macro language with the `SQL` procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code	macro variable, macro facility, proc step, autocall macro, macro definition, macro parameter, %macro parameters, macro function, dictionary tables, options statement, proc sql, macro loop, &variable	source	10
151	Health Services Research Using Electronic Health Record Data: A Grad Student How-To Paper	Ashley Collinsworth, Elisa L. Priest	Graduate students encounter many challenges when conducting health services research utilizing "real world" data obtained from electronic health records (EHRs). These challenges include cleaning and sorting data, summarizing and identifying present on admission diagnosis codes, identifying appropriate metrics for risk-adjustment, and determining the effectiveness and cost effectiveness of treatments. This paper provides graduate students with basic tools for the conduct of health services research with EHR data. We will examine SAS tools and step-by-step approaches used in an analysis of the effectiveness and cost-effectiveness of the ABCDE (Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mobility) bundle in improving outcomes for intensive care unit (ICU) patients. These tools include: 1) ARRAYS, 2) Table Look-up, 3) LAG functions, 4) `PROC TABULATE`, 5) recycled predictions, and 6) bootstrapping	lag function, proc tabulate, proc step, life table, sample, excel, regression analysis	source	9
152	Helpful Hints for Transitioning to SAS 9.4	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. And 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 you are ready to begin your transition to SAS 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, and administering the SAS 9.4 environment	customer experience, error message	source	14
153	Helping You C What You Can Do with SAS	Andrew Henrick, Karen Croft, Donald Erdman	SAS users are already familiar with the `FCMP` procedure and the flexibility it provides them in writing their own functions and subroutines. However, did you know that FCMP also allows you to call functions written in C? Did you know that you can create and populate complex C structures and use C types in FCMP? With the `PROTO` procedure, you can define function prototypes, structures, enumeration types, and even small bits of C code. This paper gets you started on how to use the `PROTO` procedure and, in turn, how to call your C functions from within FCMP and SAS	proc step, proc fcmp, cpu time, options statement	source zip	10
154	Hey! SAS Federation Server Is Virtualizing 'Big Data'!	Ivor G. Moan	In this session, we discuss the advantages of SAS Federation Server and how it makes it easier for business users to access secure data for reports and use analytics to drive accurate decisions. This frees up IT staff to focus on other tasks by giving them a simple method of sharing data using a centralized, governed, security layer. SAS Federation Server is a data server that provides scalable, threaded, multi-user, and standards-based data access technology in order to process and seamlessly integrate data from multiple data repositories. The server acts as a hub that provides clients with data by accessing, managing, and sharing data from multiple relational and non- relational data sources as well as from SAS data. Users can view data in big data sources like Hadoop, SAP HANA, Netezza, or Teradata, and blend them with existing database systems like Oracle or DB2	proc sql, big data, hadoop, web service, implicit sql pass-through, excel	source	13
155	How Best to Effectively Teach the SAS Language	Arthur X. Li	Learning a new programming language is not an easy task, especially for someone who does not have any programming experience. Learning the SAS programming language can be even more challenging. One of the reasons is that the SAS system is comprised of a variety of languages, such as `DATA STEP` language, SAS macro language, Structured Query Language for the `SQL` procedure, etc. Furthermore, each of these languages has its own unique characteristics and simply learning the syntax is not sufficient to grasp the language essence. Thus, it is not unusual to hear about someone who has learned SAS for several years and has never become a SAS programming expert. By using the `DATA STEP` language as an example, I would like to share some of my experiences on effectively teaching the SAS language	array, missing value, infile statement, output, by group processing, proc step, dataset, retain, do loop, if-then-else, automatic variable, execution phase, input statement, numeric variable, proc freq, implicit loop, raw data, txt, sas programming	source	13
156	How Do You Use Look-up Tables?	Philip R. Holland	No matter what type of programming you do in a pharmaceutical environment there will eventually be a need to combine your data with a look-up table. This look-up table could be a code list for adverse events, a list of names for visits, or just one of your own summary data sets containing totals that you will be using to calculate percentages, and you may have your favourite way to incorporate it. This paper will describe, and discuss the reasons for, using 5 different simple ways to merge data sets with look-up tables, so that, when you take over the maintenance of a new program, you will be ready for anything!	proc sql, proc step, create table, group by, order by, format statement, data merging	source	10
157	How is Your Health? Using SAS Macros, ODS Graphics, and GIS Mapping to Monitor Neighborhood and Small-Area Health Outcomes	Roshni Shah	With the constant need to inform researchers about neighborhood health data, the Santa Clara County Health Department created socio-demographic and health profiles for 109 neighborhoods in the county. Data was pulled from many public and county data sets, compiled, analyzed, and automated using SAS. With over 60 indicators and 109 profiles, an efficient set of macros was used to automate the calculation of percentages, rates, and mean statistics for all of the indicators. Macros were also used to automate individual census tracts into pre-decided neighborhoods to avoid data entry errors. Simple SQL procedures were used to calculate and format percentages within the macros, and output was pushed out using Output Delivery System (`ODS`) Graphics. This output was exported to Microsoft `EXCEL`, whi ch was used to create a sortable database for end users to compare cities and/or neighborhoods	proc step, proc sql, excel	source	12
158	How Latent Analyses within Survey Data Can Be Valuable Additions to Any Regression Model	Deanna Naomi Schreiber-Gregory	The current study looks at several w ays to investigate latent variables in longitudinal survey s and their use in logistic regression models. Three different analyses for latent variable discovery w ill be briefly review ed and explored. The procedures explored in this paper are `PROC` LCA, `PROC` LT A, `PROC` CA TMOD, `PROC` FA CTOR, `PROC` TRAJ, and `PROC` SURV EYLOGISTIC. The analyses defined through these procedures are latent profile analyses, latent class analyses, and latent transition analyses. The latent variables w ill then be included in three separate regression models. The effect of the l atent variables on the fit and use of the logistic regression model compared to a s imilar model using observed data w ill be review ed. The data used for this study w as obtained via the National Longitudinal Study of Adolescent Health, a study distributed an d collected by Add Health. Data w as analyzed using SAS 9.3. This paper is intended for any level of SAS user	latent variable, proc step, regression analysis, logistic distribution, logistic regression, factor analysis, correlation, proc surveylogistic, response variable, sample, proc means	source	9
159	How SAS Uses SAS to Analyze SAS Blogs	Chris Hemedinger	SAS blogs (hosted at http://blogs.sas.com) attract millions of page views annually. With hundreds of authors, thousands of posts, and constant chatter within the blog comments, it is impossible for one person to keep track of all of the activity. In this paper, you learn how SAS technology is use d to gather data and report on SAS blogs from the inside out. The beneficiaries include personnel from all over the company, including marketing, technical support, customer loyalty, and executives. The author describes the business case for tracking and reporting on the activity of blogging. You learn how SAS tools are used to access the WordPress database and how to create a "blog data mart" for reporting and analytics. The paper includes specific examples of the insight that you can gain from examining t he blogs analytically, and which techniques are most useful for achieving that insight	proc step, mysql, sas enterprise guide, proc sgplot, macro definition, macro facility, proc sql	source	17
160	How to Cut the Time of Processing of Ryan White Services (RSR) Data by 99% and More.	Michael Costa, Fizza Gillani	The widely used method to convert RSR XML data to some standard ready-to-process data base uses a Visual Basic mapper as a buffer tool when reading XML data, for example, into MS Access data base. This paper describes the shortcomings of this method with respect to the different schemas of RSR data and offers a SAS macro that enables users to read any schema of RSR data directly into a SAS relational data base, thus avoiding the step of creation of MS Access data base entirely. Applying our macro user can cut the time of processing of Ryan White data by 99% and more depending on the number of files that need to be processed in one run		source	6
161	How to Hunt for Utility Customer Electric Usage Patterns Armed with SAS Visual Statistics with Hadoop and Hive	Kathy Ball	Your electricity usage reveals a lot about your family and routines. Information collected from electrical smart meters captures data that can be converted into patterns of behavior that can be utilized to optimize power systems. Demand response programs represent an effective way to cope with rising energy needs and increasing electricity costs. Utilities receive customer data from smart meters, which track and store customer energy usage. The data collected is sent to the energy companies every fifteen minutes or hourly. With millions of meters deployed, this quantity of information creates a data deluge for utilities, as each customer generates about three thousand data points monthly and more than thirty-six billion reads are collected annually for a million customers. Large amounts of data must be processed to effectively control demand response, including transport, storage and access to the data	proc freq, time series analysis, proc princomp, proc step, correlation	source	12
162	How to Implement SAS 9.4 on an Amazon Web Services Cloud Server Instance	Jeff Lehmann	This paper demonstrates a tactical scenario where a SAS 9.4 test environment is installed and configured on an Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instance running Linux Red Hat version 7.1. The best practice actions to complete this SAS 9.4 implementation are based upon successful implementation techniques as well as existing customer installations	sas administrator, cpu time, red hat	source	47
163	How to Implement Two-Phase Regression Analysis to Predict Profitable Revenue Units	Shrey Tandon	Presentation	response variable, regression analysis, flag variable, logistic regression, logistic distribution, data cleaning, proc reg, sas enterprise guide, correlation, standard deviation	source	15
164	How to Maintain Happy SAS Users: A SAS Environment Primer for IT Professionals	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 will be used by your users is the first step. The guidance offered in this paper will help 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
165	How to Tell the Best Story with Your Data Using SAS Visual Analytics Graph Builder	Travis Murphy	How do you engage your report viewer on an emotional and intellectual level and tell the story of your data? You create a perfect graphic to tell that story using SAS Visual Analytics Graph Builder. This paper takes you on a journey by combining and manipulating graphs to refine your data's best possible story. This paper shows how layering visualizations can create powerful and insightful viewpoints on your data. You see how to create multiple overlay graphs, single graphs with custom options, data -driven lattice graphs, and user-defined lattice graphs to vastly enhance the story-telling power of your reports and dashboards. Some examples of custom graphs covered in this paper are resource timelines combined with scatter plots and bubble plots to enhance project reporting, butterfly charts combined with bubble plots to provide a new way to show demographic data, and bubble change plots to highlight the journey your data has traveled	bubble plot, scatter plot, bar chart, sample, data visualization	source	23
166	How to Use Internal and External Data to Realize the Potential for Changing the Game in Handset Campaigns	Hurcan Coskun	Telecommunication industry is the fastest changing business ecosystem in this century. Therefore handset loyalty programs are the key to keep customers. However, these handset campaigns have great fraud and payment risks if the companies do not have the ability of classifying and assessing customers properly according to their risk propensity. In order to solve this problem , risk management departments have to predict risky customers and then integrate risk scores and all information into one real time dec ision engine for optimized handset application vetting. After an assessment period, SAS Analytics platform and RTDM was chosen most suitable solution as it provides a flexible user friendly interface, high integration and fast deployment capability. After completion of the project , %13 of all handse t sales was concluded via RTDM in just 3 months. Other benefit of RTDM is 24% percent cost saving in external data inquiries	data mining, predictive analytics, analytics platform, telecom industry	source	8
167	How to Use SAS for GMM Logistic Regression Models for Longitudinal Data with Time-Dependent Covariates	Katherine Cai	In longitudinal data, it is important to account for the correlation due to repeated measures and time- dependent covariates. Generalized method of moments can be used to estimate the coefficients in longitudinal data, although there are currently limited procedures in SAS to produce GMM estimates for correlated data. In a recent paper, Lalonde, Wilson, and Yin provided a GMM model for estimating the coefficients in this type of data. SAS `PROC IML` was used to generate equations that needed to be solved to determine which estimating equations to use. In addition, this study extended classifications of moment conditions to include a type IV covariate. Two data sets were evaluated using this method, including re - hospitalization rates from a Medicare database as well as body mass index and future morbidity rates among Filipino children. Both examples contain binary responses, repeated measures, and time - dependent covariates	logistic regression, correlation, logistic distribution, regression analysis, time-dependent covariate, proc genmod, estimate, proc mixed, proc step, macro call, generalized estimating equations, proc glimmix, binary variable, longitudinal data, random intercept	source	8
168	HTML5 and SAS Mobile BI: Empowering Business Managers with Analytics and Business Intelligence	Murali Nori	Business managers are seeing the value of incorporating business information and analytics in daily decision- making with real-time information, when and where it is needed during business meetings and customer engagements. Real-time access of customer and business information reduces the latency in decision-making with confidence and accuracy, increasing the overall efficiency of the company. With SAS Visual Analytics 7.2, SAS is introducing new HTML5 based web viewer interface and adding advanced features in the SAS Mobile BI native mobile application, to enhance the reach and experience of business managers to SAS analytics and dashboards from SAS Visual Analytics. This presentation focuses on both the new HTML5 based web viewer and the new advancements made with SAS Mobile BI that empowers business users. I will present in detail, the new features that are offered with the HTML5 based viewer and with SAS Mobile BI		source	9
169	I Object: SAS Does Objects with DS2	Peter W. Eberhardt, Xue Yao	The `DATA STEP` has served SAS programmers well over the years, and although it is powerful, it has not fundamentally changed. With DS2, SAS has introduced a significant alternative to the `DATA STEP` by introducing an object -oriented programming environment. In this paper, we share our experiences with getting started with DS2 and learnin g to use it to access, manage, and share data in a scalable, threaded, and standards -based way	missing value, macro function, macro variable, proc step, sas function, dataset, return, sysdate9, sas programmer, proc sql	source	27
170	Identifying Factors Associated with High-Cost Patients	Jeff Dang	Research has shown that the five percent of patients with the highest individual medical costs account for nearly fifty percent of the total health care expenditures in the United States (NIHCM Foundation, 2012) . Using SAS Enterprise Guide and `PROC LOGISTIC`, a statistical methodology was developed to identify key factors (for example, patient demographics, diagnostic symptoms, co -morbidity, and the type of procedure) associated with the high cost of health care. Analyses were performed using FAIR Health's National Private I nsurance Claims (FH NPIC ) database, which contains information on health care utilization and cost in the United States. The analyses focused on chronic conditions, such as Coronary Heart Disease (CHD) and Chronic Obstructive Pulmonary Disease (COPD) . Furthermore, heat map and info graphs were created using SASi Visual Analytics to illustrate areas with potentially high-cost patients with chronic diseases across the nation	logistic regression, logistic distribution, regression analysis, sas enterprise guide, proc logistic, proc step	source zip	12
171	Implementing a Discrete Event Simulation Using the American Community Survey and the SAS University Edition	Michael C. Grierson	SAS University Edition is a great addition to the world of freely available analytic software, and this 'how- to' presentation shows you how to implement a discrete event simulation using Base SAS to model future US Veterans population distributions. Features include generating a slideshow using `ODS` output to PowerPoint	sas base, macro call, rand, life table, sample, group by	source zip	12
172	Important Things to Know when Deploying SAS Grid Manager	Doug Haigh, Glenn Horton	SAS Grid Computing promises many benefits that the SAS community has been demanding for years, including workload management of SAS applications, a highly available infrastructure, higher resource utilization, flexibility for IT infrastructure, and potentially improved performance of SAS applications. To implement these benefits, you need to have a good definition of what you need and an understanding of what is involved in enabling the SAS tasks to take advantage of all the SAS Grid nodes. In addition to having this understanding of SAS, the underlying hardware infrastructure (cores to storage) must be configured and tuned correctly. This paper discusses the most important things (or misunderstandings) that SAS customers need to know before they deploy SAS Grid Manager	sas grid, grid computing, data mining	source	5
173	Improving SAS Global Forum Papers	Vijay Singh, Pankush Kalgotra, Goutam Chakraborty	Just as research is built on existing research, the references section is an important part of a research paper. The purpose of this study is to find the differences between professionals and academicians with respect to the references section of a paper. Data is collected from SAS Global Forum 2014 Proceedings. Two research hypotheses are supported by the data. First, the average number of references in papers by academicians is higher than those by professionals. Second, academicians follow standards for citing references more than professionals. Text mining is performed on the references to understand the a ctual content. This study suggests that authors of SAS Global Forum papers should include more references to increase the quality of the papers	proc freq, alternative hypothesis	source	7
174	Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner	Ricardo Galante	In data mining modelling, data preparation is the most crucial, most difficult, and longest part of the mining process. A lot of steps are involved. Consider the simple distribution analysis of the variables, the diagnosis and reduction of the influence of variables' multicollinearity, the imputation of missing values, and the construction of categories in variables. In this presentation, we use data mining models in different areas like marketing, insurance, retail and credit risk. We show how to implement data preparation through SAS Enterprise Miner, using different approaches. We use simple code routines and complex processes involving statistical insights, cluster variables, transform variables, graphical analysis, decision trees, and more	missing value, feature selection, proc princomp, data mining, interval variable, covariance, correlation, response variable, chi-square test, data wrangling, imputation, proc varclus	source	21
175	Improving the Performance of Two-Stage Modeling Using the Association Node of SAS Enterprise Miner 12.3	Girish Shirodkar, Goutam Chakraborty, Ankita Chaudhari	Over the years, few published studies have discussed ways to improve the performance of two -stage predictive models. This study is an attempt to demonstrate how we can leverage the Association node in SAS Enterprise Miner to improve the classification accuracy of a two-stage model. We find that creating dummy variables to capture association rules with lifts higher than1.6 and using those as potential input variables improve fit statistics of both the first and the second stage models of the two stage model. For example, for the first stage model, the validation ASE improved to 0.228 from 0.238, cumulative lift to 1.56 from 1.40. For the second stage model, the misclassification rate improved to 0.240 from 0.243 and the final prediction error to 0.17 from 0.18	binary variable, logistic regression, data mining, association rules, logistic distribution, regression analysis, residual, response variable, data cleaning	source	9
176	Imputing Missing Data using SAS	Christopher Yim	Missing data is an unfortunate reality of statistics. However, there are various ways to estimate and deal with missing data. This paper explores the pros and cons of traditional imputation methods vs maximum likelihood estimation as well as singular versus multiple imputation. These differences are displayed through comparing parameter estimates of a known dataset and simulating random missing data of different severity. In addition, this paper utilizes the SAS procedures `PROC MI` and `PROC MIANALYZE` and shows how to use these procedures in a longitudinal data set	imputation, estimate, regression analysis, multiple imputation, proc step, proc mi, missing value, maximum likelihood estimation, missing at random, standard deviation, likelihood function, sample, confidence interval, mse, sample size, proc mianalyze, proc calis, covariance, predictor variable, response variable	source	9
177	Incorporating External Economic Scenarios into Your CCAR Stress Testing Routines	Kenneth Sanford, Christian Macaro	Since the financial crisis of 2008, banks and bank holding companies in the United States have faced increased regulation. One of the recent changes to these regulations is known as the Comprehensive Capital Analysis and Review (CCAR). At the core of these new regulations, specifically under the Dodd- Frank Wall Street Reform and Consumer Protection Act and the stress tests it mandates, are a series of "what-if" or "scenario analyses" requirements that involve a number of scenarios provided by the Federal Reserve. This paper proposes frequentist and Bayesian time series methods that solve this stress testing problem using a highly practical top-down approach. The paper focuses on the value of using univariate time series methods, as well as the methodology behind these models	stochastic economic scenario, stress testing, regression analysis, time series analysis, likelihood function, interest rate, proc reg, posterior probability, feature selection, economic capital, expected loss, lag function, macro-economic scenario, response variable, prior probability	source	10
178	Inside the DATA Step: Pearls of Wisdom for the Novice SAS Programmer	Joshua M. Horstman, Britney D. Gilbert	Why did my merge fail? How did that variable get truncated? Why am I getting unexpected results? Understanding how the `DATA STEP` actually works is the key to answering these and many other questions. In this paper, two independent consultants with a combined three decades of SAS programming experience share a treasure trove of knowledge aimed at helping the novice SAS programmer take their game to the next level by peering behind the scenes of the `DATA STEP`. We'll touch on a variety of topics including compilation vs. execution, the program data vector, and proper merging techniques, with a focus on good programming practices that will help the programmer steer clear of common pitfalls	sas programmer, output, pdv, character variable, merge statement, retain, compilation phase, length statement, attrib statement, numeric variable, sas log, proc step, set statement, substr function, sas programming, by group processing, execution phase	source	11
179	Integrating Data and Business Rules with a Control Data Set in SAS	Edmond Cheng	In SAS software development, data specifications and process requirements can be built into a user - defined control data set functioning as components of ETL routines. A control data set provides comprehensive definition on the data source, relationship, logic, description, and metadata of each data element. This approach facilitates auto-generated SAS codes during program execution to perform data ingestion, transformation, and loading procedures based on rules defined in the table. This paper demonstrates the application of using a control data set for the following: (1) data table initialization and integration; (2) validation and quality control; (3) element transformation and creation; (4) data loading; and (5) documentation. SAS programmers and business analysts would fi nd programming development and maintenance of business rules more efficient with this standardized method	data cleaning, macro variable, data validation rules, proc step, data controls, quality control, proc sql, sas syntax	source	11
180	Integrating SAS and the R Language with Microsoft SharePoint	Piyush K. Singh, Prasoon Sangwan, Shiv Govind Yadav	Microsoft SharePoint has been adopted by a number of companies today as their content management tool because of its ability to create and manage documents, records, and web content. It is described as an enterprise collaboration platform with a variety of capabilities, and thus it stands to reason that this platform should also be used to surface content from analytical applications such as SAS and the R language. SAS provides various methods for surfacing SAS content through SharePoint. This paper describes one such methodology that is both simple and elegant, requiring only SAS Foundation. It also explains how SAS and R can be used together to form a robust solution for delivering analytical results. The paper outlines the approach for integrating both languages into a single security model that uses Microsoft Active Directory as the primary authentication mechanism for SharePoint. It also describes how to extend the authorization to SAS running on a Linux server where LDAP is used	web service, sample, r programming	source	11
181	Introduce a Linear Regression Model by Using the Variable Transformation Method	Nancy Hu	Presentation	proc reg, data transformation, proc step, regression analysis, correlation, proc genmod, response variable, proc glm, predictor variable	source zip	12
182	Introducing the HPGENSELECT Procedure: Model Selection for Generalized Linear Models and More	Gordon Johnston, Bob Rodriguez	Generalized linear models are highly useful statistical tools in a broad array of business applications and scientific fields. How can you select a good model when numerous models that have different regression effects are possible? The `HPGENSELECT` procedure, which was introduced in `SAS/STAT` 12.3, provides forward, backward, and stepwise model selection for generalized linear models. In `SAS/STAT` 14.1, the `HPGENSELECT` procedure also provides the LASSO method for model selection. Y ou can specify common distributions in the family of generalized linear models, such as the Poisson, binomial, and multinomial distributions. Y ou can also specify the Tweedie distribution, which is important in ratemaking by the insurance industry and in scientific applications. Y ou can run the `HPGENSELECT` procedure in single-machine mode on the server where `SAS/STAT` is installed	proc hpgenselect, binomial distribution, poisson distribution, proc genmod	source	18
183	Introduction to SAS Data Loader: The Power of Data Transformation in Hadoop	Keith Renison	Organizations are loading data onto Hadoop platforms at an extraordinary rate. However, in order to extract value from these platforms, the data must be prepared for analytic exploitation. As the volume of data grows, it becomes increasingly more important to reduce data movement, as well as to leverage the computing power of these distributed systems. This paper provides a cursory overview of SAS Data Loader, a product specifically aimed at these challenges. We cover the underlying mechanisms of how SAS Data Loader works, as well as how it's used to profile, cleanse, transform, and ultimately prepare data for analytics in Hadoop	data cleaning, data profiling	source	7
184	Introduction to SAS Hash Objects	Christopher W. Schacherer	The SAS hash object is an incredibly powerful technique for integrating data from two or more datasets based on a common key. The current work describes the basic methodology for defining, populating, and utilizing a hash object to perform lookups within the `DATA STEP` and provides examples of situations in which the performance of SAS programs is improved by their use. Common problems encountered when using hash objects are explained, and tools and techniques for optimizing hash objects within your SAS program are demonstrated	hash table, cpu time, length statement, temporary array, proc sql, key, set statement, proc step, memory usage, proc means, where statement, execution phase, reference data	source	26
185	Jeffreys Interval for One-Sample Proportion with SAS/STAT Software	Wu Gong	This paper introduces Jeffreys interval for one-sample proportion using SAS software. It compares the credible interval from a Bayesian approach with the confidence interval from a frequentist approach. Different ways to calculate the Jeffreys interval are presented using `PROC FREQ`, the QUANTILE function, a SAS program of random walk Metropolis sampler, and `PROC MCMC`	sample, credible interval, confidence interval, binomial distribution, proc step, likelihood function, posterior probability, prior probability, parameter, sas output, bayesian statistics, normal distribution, beta distribution, quantile, quantile functions	source	7
186	Joining Tables Using SAS Enterprise Guide	Anita Measey	SAS Enterprise Guide provides a point and click interface that allows users to manipulate and analyse data without having to write code. But what do you do when your data is not "perfect"? The following examples will show you how to combine data that is "perfect"; where your data types and lengths match and your variable names are the same; where your data types don't match; include a "having" clause to your grouped data; and adding a subquery to your results. All examples presented were created in SAS Enterprise Guide 5.1 and are intended for a beginning level	where statement, sas enterprise guide	source	6
187	Kaplan-Meier Survival Plotting Macro %NEWSURV	Jeffrey P. Meyers	The research areas of pharmaceuticals and oncology clinical trials greatly depend on time-to-event endpoints such as overall survival and progression-free survival. One of the best graphical displays of these analyses is the Kaplan- Meier curve, which can be simple to generate with the `LIFETEST` procedure but difficult to customize. Journal articles generally prefer that statistics such as median time-to-event, number of patients, and time-point event-free rate estimates be displayed within the graphic itself, and this was previously difficult to do without an external program such as Microsoft `EXCEL`. The macro NEWSURV takes advantage of the Graph Template Language (GTL) that was added with the SG graphics engine to create this level of customizability without the need for backend manipulation. Taking this one step further, the macro was improved to be able to generate a lattice of multiple unique Kaplan-Meier curves for side by side comparisons or condensing figures for publications	macro parameter, class variable, macro call, hazard ratio, proc phreg, graph template language, proc step, time variable, confidence interval, character variable, numeric variable, proc means, where statement, kaplan-meier plot, treatment effect, data transformation, macro variable, overall survival, progression-free survival, excel, regression analysis, proc sql	source	20
188	Killing Them with Kindness: Policies Not Based on Data Might Do More Harm Than Good	Tuesdi Helbig, Matthew Foraker	Educational administrators sometimes have to make decisions based on what they believe is in the best interest of their students because they do not have the data they need at the time. Some administrators do not even know that the data exist to help them make their decisions. However, well -intentioned policies that are not based on facts can sometimes do more harm than good for the students and the institution. This presentation discusses the results of the policy analyses conducted by the Office of Institutional Research at Western Kentucky University using Base SAS, `SAS/STAT`, SAS Enterprise Miner, and SAS Visual Analytics	proc hpsplit, anova, box plot, data mining, one-way anova, bar chart, predictor variable	source	22
189	Lasso Regularization for Generalized Linear Models in Base SAS Using Cyclical Coordinate Descent	Robert Feyerharm	The cyclical coordinate descent method is a simple algorithm that has been used for fitting generalized linear models with lasso penalties by Friedman et al. (2007). The coordinate descent algorithm can be implemented in Base SAS using array processing to perform efficient variable selection and shrinkage for GLMs with the L1 penalty (the lasso)	regression analysis, elastic net, logistic regression, logistic distribution, proc reg, feature selection, sas base, ridge regression, proc genmod, poisson regression, estimate, poisson distribution, data cleaning	source	10
190	Lasso Your Business Users by Designing Information Pathways to Optimize Standardized Reporting in SAS Visual Analytics	Stephen M. Overton	SAS Visual Analytics opens up a world of intuitive interactions, providing report creators the ability to develop more efficient ways to deliver information. Business-related hierarchies can be defined dynamically in SAS Visual Analytics to group data more efficiently-no more going back to the developers. Visualizations can interact with each other, with other objects within other sections, and even with custom applications and SAS stored processes. This paper provides a blueprint to streamline and consolidate reporting efforts using these interactions available in SAS Visual Analytics. The goal of this methodology is to guide users down information pathways that can progressively subset data into smaller, more understandable chunks of data, while summarizing each layer to provide insight along the way. Ultimately the final destination of the information pathway holds a reasonable subset of data so that a user can take action and facilitate an understood outcome	account activity, raw data, bar chart, account	source	15
191	Latest and Greatest: Best Practices for Migrating to SAS 9.4	Alec S. Fernandez	SAS customers benefit greatly when they are using the functionality, performance, and stability available in the latest version of SAS. However, the task of moving all SAS collateral such as programs, data, catalogs, metadata (stored processes, maps, queries, reports, and so on), and content to SAS 9.4 can seem daunting. This paper provides an overview of the steps required to move SAS collateral from systems based on SAS 9.2 and SAS 9.3 to the current release of SAS 9.4	error message, credit migration, data cleaning	source	15
192	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 typically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG function, 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, time series analysis, by group processing, missing value, hash iterator	source zip	14
193	Learn Hidden Ideas in Base SAS to Impress 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. Learning SAS over 30+ years, I have collected a few, and I offer them to you in this presentation	sas base, options statement, proc sql, sas log, dictionary tables, variable list, proc step, macro variable, sas batch, macro function, sample	source	10
194	Learning Analytics to Evaluate and Confirm Pedagogic Choices	Richard Self	This chapter will describe, evidence and critically evaluate a pedagogical journey which takes the reader from a traditional position of the "academic as a domain expert" to what turns out to be a far more effective position as the "academic as a Learning to Learn expert". The evidence used to confirm the value and effectiveness of this changed approach by the author over a period of 5 years or so is based on Learning Analytics which demonstrate significantly improved academic results and achievements. The confirmatory evidence derived from the Learning Analytics is now being used to make informed choices by other colleagues to change their own pedagogical choices to also develop excellent achievement in their own modules and programmes. It is also being used to guide the design of new learning spaces in a new STEM facility that is in development at the University	proc ttest, excel, proc step, standard deviation	source	13
195	Leveraging SAS Environment Manager for SAS Customer Intelligence Implementations	Daniel Alvarez	SAS Environment Manager helps SAS administrators and system administrators manage SAS resources and effectively monitor the environment. SAS Environment Manager provides administrators with a centralized location for accessing and monitoring the SAS Customer Intelligence environment. This enables administrators to identify problem areas and to maintain an in-depth understanding of the day-to-day activities on the system. It is also an excellent way to predict the usage and growth of the environment for scalability. With SAS Environment Manager, administrators can set up monitoring for SAS Customer Intelligence logs (for example, SASCustIntelCore6.2.log, SASCustIntelStudio6.3.log) and other general logs from the SAS Intelligence Platform. This paper contains examples for administrators who support SAS Customer Intelligence to set up this type of monitoring. It provides recommendations for approaches and for how to interpret the results from SAS Environment Manager	data mart, error message	source	12
196	Location-Based Association of Customer Sentiment and Retail Sales	Asish Satpathy, Goutam Chakraborty, Tanvi Kode	There are various economic factors that affect retail sales. One important factor that is expected to correlate with sales is customers' overall sentiments towards a given brand. In this paper, we analyze how location specific customers' sentiments could correlate with sales at retail stores. In our attempt to find any dependency, we have used location specific Twitter feeds related to a national brand chain retail store (Walmart Inc.) and opinion mine their overall sentiments using SAS Sentiment Analysis Studio. We estimate correlation between peoples' sentiments and retail sales within the studied geographic areas. Later in the analysis, using ESRI's ArcGIS Online, we esti mate if other location specific variables that could potentially correlate with customer's sentiments towards the brand, are significantly important to predict brand's retail sales	sample, correlation, regression analysis	source	6
197	Lost in the Forest Plot? Follow the Graph Template Language AXISTABLE Road!	Prashant Hebbar	A forest plot is a common visualization for meta-analysis. Some popular versions that use subgroups with indented text and bold fonts can seem outright daunting to create. With SAS 9.4, the Graph Template Language (GTL) has introduced the AXISTABLE statement, specifically designed for including text data columns into a graph. In this paper, we demonstrate the simplicity of cr eating various forest plots using AXISTABLE statements. Come and see how to create forest plots as clear as day!	forest plot, scatter plot, proc sgplot, odds ratio, confidence interval	source zip	11
198	Maintaining a 'Look and Feel' throughout a Reporting Package Created with Diverse SAS Products	Barbara B. Okerson	SAS provides a number of tools for creating customized professional reports. While SAS provides point - and-click interfaces through products such as SAS Web Report Studio, SAS Visual Analytics or even SAS Enterprise Guide, unfortunately, many users do not have access to the high-end tools and require customization beyond the SAS Enterprise Guide point-and-click interface. Fortunately, Base SAS procedures such as the `REPORT` procedure, combined with graphics procedures, macros, `ODS`, and Annotate can be used to create very customized professional reports. When toggling together different solutions such as SAS Statistical Graphics, the `REPORT` procedure, `ODS`, and `SAS/GRAPH`, different techniques need to be used to keep the same look and feel throughout the report package. This presentation looks at solutions that can be used to keep a consistent look and feel in a report package created with different SAS products	proc step, sample, ods pdf, proc report, proc sgpanel	source	9
199	Make Better Decisions with Optimization	David R. Duling	Automated decision making systems are now found everywhere, from your bank to your government to your home. For example, when you inquire for a loan through a website, a complex decision process likely runs combinations of statistical models and business rules to make sure you are offered a set of options for tantalizing terms and conditions. To make that happen, analysts diligently strive to encode their complex business logic into these systems. But how do you know if you are making the best possible decisions? How do you know if your decisions conform to your business constraints ? You might want to maximize the number of loans that you provide while balancing the risk among different customer categories. Welcome to the world of optimization. SAS Business Rules Manager and `SAS/OR` software can be used together to manage and optimize decisions. This presentation demonstrates how to build business rules and then optimize the rule parameters to maximize the effectiveness of those rules	data validation rules, proc optlso, histogram	source zip	10
200	Managing Extended Attributes With a SAS Enterprise Guide Add-In	Larry Hoyle	SAS 9.4 introduced extended attributes, which are name-value pairs that can be attached to either the data set or to individual variables. Extended attributes are managed through `PROC DATASETS` and can be viewed through `PROC CONTENTS` or through Dictionary.XATTRS. This paper describes the development of a SAS Enterprise Guide custom add-in that allows for the entry and editing of extended attributes, with the possibility of using a controlled vocabulary. The controlled vocabulary used in the initial application is derived from the lifecycle branch of the Data Documentation Initiative metadata standard (DDI-L)	proc step, sas enterprise guide, dataset, format statement, proc freq	source	9
201	Managing Files by Click and Drag?	Jason Wachsmuth	File management is a tedious process, but it can be automated by using SAS to create and execute a Windows Command Script. The macro in this paper copies files from one location to another, identifies obsolete files by the version number, and then moves them to an archive folder. Assuming some basic conditions are met, this macro is intended to be easy to use and robust. Windows users who run routine programs for projects with rework may want to consider this solution	macro quoting function, length statement	source	5
202	Managing Qualtrics Survey Distributions and Response Data with SAS	Faith Parsons, Sean Mota, Yan Quan	Managing multiple surveys and distributing them to several research participants at multiple time intervals can be tedious and time consuming. Qualtrics is an online survey tool that offers a variety of features that could be useful for researchers. By using SAS to interface with the Qualtrics REST API, and SAS XML Mapper to generate the XML maps from the XML responses, we were able to implement an effective system that enabled survey data to be accessed regularly, accurately scheduled the distribution of surveys to research participants, and prevented withdrawn participants from receiving scheduled emails. In this paper, we demonstrate how to implement the submit API requests with the `FILENAME` statement (URL access method) and process API responses with SAS XML Mapper. Additionally we describe an	rest api, filename statement, dataset	source	7
203	Managing SAS Web Infrastructure Platform Data Server High-Availability Clusters	Ken Young	The SAS Web Application Server is a lightweight server that provides enterprise -class features for running SAS middle-tier web applications. This server can be configured to use the SAS Web Infrastructure Platform Data Server for a transactional storage database. You can meet the high- availability data requirement in your business plan by implementing a SAS Web Infrastructure Platform Data Server cluster. This paper focuses on how the SAS Web Infrastructure Platform Data Server on the SAS middle tier can be configured for failover and data replication involving multiple nodes. SAS Environment Manager and pgpool-II are used to enable these high-availability strategies, to monitor the server status, and to initiate failover as needed	business intelligence, connection string	source	18
204	Mapping out SG Procedures and Using PROC SGPLOT for Mapping	Frank Poppe	This paper describes the main functions of the SAS Statistical Graphics (SG) procedures (also referred to as SAS `ODS` Graphics procedures) and how they are related. It also offers a way to create geographic maps with regions colored through response variables. SAS users who have experience with `SAS/GRAPH` procedures will hesitate to change over to SG procedures. However, for virtually all tasks that you can perform with the well-known `SAS/GRAPH` procedures, the corresponding SG procedures are easily pointed out, frequently providing you with even more enhanced features. And, because the SG procedures continue to be enhanced with new features and the appearance of many elements is governed by the `ODS` styles, they are very well suited to providing a consistent style across all your output, text, and graphics. The paper first describes the `SGPLOT` procedure, and then moves on to the more elaborate possibilities of the SGPANEL and SGSCATTER procedures	proc step, proc sgplot, proc sgpanel, proc gmap, proc template, response variable, proc sgscatter, proc sgdesign, by group processing, sas enterprise guide, line chart	source zip	14
205	Marketing at the Speed of Gaming: Real-Time Decisions for Casinos	Natalie G. Osborn	There are few business environments more dynamic than that of a casino. Serving a multitude of entertainment options to thousands of patrons every day results in a lot of customer interaction points. All of these interactions occur in a highly competitive environment where, if a patron does not feel that he is getting the recognition that he deserves, he can easily walk across the street to a competitor. Add to this the expected amount of reinvestment per patron in the forms of free meals and free play. Making high- quality real-time decisions during each customer interaction is critical to the success of a casino. Such decisions need to be relevant to customers' needs and values, reflect the strategy of the business, and help maximize the organization's profitability. Being able to make those decisions repeatedly is what separates highly successful businesses from those that flounder or fail	data warehouse, predictive analytics	source	4
206	Master Data and Command Results: Combine Master Data Management with SAS Analytics for Improved Insights	Ron Agresta	It's well known that SAS is the leader in advanced analytics but often overlooked is the intelligent data preparation that combines information from disparate sources to enable confident creation and deployment of compelling models. Improving data-based decision making is among the top reasons why organizations decide to embark on master data management (MDM) projects and why you should consider incorporating MDM functionality into your analytics-based processes. MDM is a discipline that includes the people, processes, and technologies for creating an authoritative view of core data elements in enterprise operational and analytic systems. This paper demonstrates why MDM functionality is a natural fit for many SAS solutions that need to have access to timely, clean, and uni que master data. Because MDM shares many of the same technologies that power SAS analytic solutions, it has never been easier to add MDM capabilities to your advanced analytics projects	master data management, data cleaning, data validation rules, raw data	source	6
207	Member-Level Regression Using SAS Enterprise Guide and SAS Forecast Studio	Leigh McCormack, Prudhvidhar Perati	The need to measure slight changes in healthcare cost and utilization patterns over time is vital in predictive modeling, forecasting, and other advanced analytics. At BlueCross BlueShield of Tennessee, Inc., an independent company of BlueCross BlueShield Association, a method for developing member level forecasts creates a better way of identifying these changes across various time spans. The goal is to create multiple metrics at the member level that will indicate when an individual is seeking more or les s medical or pharmacy services. Significant increases or decreases in utilization and cost are used to predict the likelihood of acquiring certain conditions, seeking services at particular facilities, and self - engaging in health and wellness. Data setup and compilation consists of calculating a member's eligibility with the health plan and then aggregating cost and utilization of particular services ( i.e., primary care physician visits, pharmacy costs, emergency room visits)	time series analysis, missing value, sas enterprise guide, time series data, insurance claims, sas base, proc sql	source	8
208	Meter Data Analytics--Enabling Actionable Decisions to Derive Business Value from Smart Meter Data	Tom Anderson, Jennifer Whaley	A utility's meter data is a valuable asset that can be daunting to leverage. Consider that one household or premise can produce over 35,000 rows of information, consisting of over 8 MB of data per year. Thirty thousand meters collecting fifteen-minute-interval data with forty variables equates to 1.2 billion rows of data. Using SAS Visual Analytics, we provide examples of leveraging smart meter data to address business around revenue protection, meter operations, and customer analysis. Key analyses include identifying consumption on inactive meters, potential energy theft, and stopped or slowing meters; and support of all customer classes (for example, residential, small commercial, and industrial) and their data with different time intervals and frequencies	time series analysis, proc freq, data science	source	11
209	Methodological and Statistical Issues in Provider Performance Assessment	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
210	Methodology of Model Creation	Peter Kertys	The goal of this session is to describe the whole process of model creation from the business request through model specification, data preparation, iterative model creation, model tuning, implementation, and model servicing. Each mentioned phase consists of several steps in which we describe the main goal of the step, the expected outcome, the tools used, our own SAS codes, useful nodes, and settings in SAS Enterprise Miner, procedures in SAS Enterprise Guide, measurement criteria, and expected duration in "man-days." For three steps, we also present deep insights with examples of practical usage, explanations of used codes, settings, and ways of exploring and interpreting the output. During the actual model creation process, we suggest using Microsoft `EXCEL` to keep all input metadata along with information about transformations performed in SAS Enterprise Miner	data mining, sas enterprise guide, excel, model documentation, proc step, data wrangling, correlation, proc contents, proc sql, box plot	source	11
211	Model Risk and Corporate Governance of Models with SAS	Boaz Galinson	Lending is the core business of most banks. One of the key success factors in credit is to be able to differentiate between the good borrowers and the bad ones by using credit rating models (estimating probability of default, exposure at default and loss given default) you can trust. These models may be input to other models such as credit pricing models, concentration risk models, economic capital models, etc. As a result, a flawed model may have a severe impact on decision-making when it is based on models. Therefore development of corporate governance framework for models along with specific IT tools to monitor and mitigate that risk,is required	credit risk, concentration risk	source	4
212	Modeling Effect Modification and Higher-Order Interactions: A Novel Approach for Repeated Measures Design Using the LSMESTIMATE Statement in SAS 9.4	Pronabesh DasMahapatra, Ryan Black	Effect modification (moderation) occurs when the association between a predictor of interest and the outcome, is differential across levels of a third variable - the modifier. Effect modification is statistically tested as the interaction effect between the predictor and the modifier. In repeated measures studies (with more than 2 time points), higher-order (three-way) interactions must be considered to test effect modification by adding time to the interaction terms. Custom fitting and constructing these repeated measures models are difficult and time consuming, especially with respect to estimating post-fitting contrasts. With the advancement of LSMESTIMATE statement in SAS, a simplified approach can be used to custom test for higher-order interactions with post-fitting contrasts within a repeated measures framework	fixed effects, proc glimmix	source zip	6
213	Modeling to Improve the Customer Unit Target Selection for Inspections of Commercial Losses in the Brazilian Electric Sector: The case of CEMIG	Sergio Henrique Ribeiro, Iguatinan Monteiro	Electricity is an extremely important product for the society. In Brazil, the electric sector is regulated by ANEEL (Agência Nacional de Energia Elétrica) and one of the regulated aspects is the power loss in the distribution system. In 2013, 13,99% of all injected energy was lost in the Brazilian system. The commercial loss is one of the power losses classifications, which can be countered by inspections of the electrical installation in the search for irregularities in power meters. The CEMIG (Companhia Energética de Minas Gerais) currently serves approximately 7.8 million customers, which makes unfeasible to inspect all customer units, in financial and logistic terms. Thus is essential the selection of the potential inspection targets. In this paper, logistic regression models, decision tree and Ensemble were used to improve the target selection process in CEMIG. The results indicate an improvement in the positive predictive value from 35% to 50%	proc hpsplit, logistic distribution, data mining, predictor variable, precision, logistic regression, data cleaning, proc princomp	source	7
214	Modelling Operational Risk Using Extreme Value Theory and Skew t-Copulas via Bayesian Inference Using SAS	Betty Johanna Garzon Rozo	Operational risk losses are heavy tailed and likely to be asymmetric and extremely dependent among business lines and event types. We propose a new methodology to asses s, in a multivariate way, the asymmetry and extreme dependence between severity distributions and to calculate the capital for operational risk. This methodology simultaneously uses several parametric distributions and an alternative mix distribution (the lognormal for the body of losses and the generalized Pareto distribution for the tail) via the extreme value theory using SAS; the multivariate skew t-copula applied for the first time to operational losses; and the Bayesian inference theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyzes a new operational loss data set, SAS Operational Risk Global Data (SAS OpRisk Global Data), to model operational risk at international financial institutions. All of the severity models are constructed in SAS 9.2	operational risk, proc freq, excess of loss, missing value, operational loss, probability distribution, proc step, weibull distribution, likelihood function, cumulative frequency, goodness of fit, feature selection, sample, frequency analysis, loss event, maximum likelihood estimation, estimate, retail banking	source	20
215	Modernise Your SAS Platform	David Shannon	Organisations find SAS upgrades and migration projects come with risk, costs and challenges to solve . The benefits are enticing new software capabilities such as SAS Visual Analytics, which help maintain your competitive advantage. An interesting conundrum. This paper explores how to evaluate the benefits, plan the project and how the cloud option affects modernisation. The author presents with the experience of delivering numerous migration and modernisation projects from the leading UK SAS Implementation Partner	sas administrator, batch job	source	11
216	More Data, Less Chatter: Improving Performance on z/OS via IBM zHPF	Lewis King, Fred R. Forst	This paper describes how we reduced elapsed time for the third maintenance release for SAS 9.4 by as much as 22% by using the High Performance FICON for IBM System z (zHPF) facility to perform I/O for SAS files on IBM mainframe systems. The paper details the performance improvements, internal testing to quantify improvements, and the customer actions needed to enable zHPF on their system. The benefits of zHPF are discussed within the larger context of other techniques that a customer can use to accelerate processing of SAS files	elapsed time, sas library	source	18
217	Multilevel Models for Categorical Data Using SAS PROC GLIMMIX: The Basics	Mihaela Ene, Bethany A. Bell, Genine L. Blue, Elizabeth Leighton	Multilevel models (MLMs) are frequently used in social and health sciences where data are typically hierarchical in nature. However, the commonly used hierarchical linear models (HLMs) are appropriate only when the outcome of interest is normally distributed. When you are dealing with outcomes that are not normally distributed (binary, categorical, ordinal), a transformation and an appropriate error distribution for the response variable needs to be incorporated into the model. Therefore, hierarchical generalized linear models (HGLMs) need to be used. This paper provides an introduction to specifying HGLMs using `PROC GLIMMIX`, following the structure of the primer for HLMs previously presented by Bell, Ene, Smiley, and Schoeneberger (2013). A brief introduction into the field of multilevel modeling and HGLMs with both dichotomous and polytomous outcomes is followed by a discussion of the model- building process and appropriate ways to assess the fit of these models	proc glimmix, proc step, likelihood function, fixed effects, covariance, proc mixed, random intercept, estimate, predictor variable, odds ratio, proc genmod, sample, covariance structure, multinomial distribution, response variable, random slope, correlation, maximum likelihood estimation	source	12
218	Multinomial Logistic Model for Long-Term Value	Bruce S. Lund	Customer Long-Term Value (LTV) is a concept that is readily explained at a high level to marketing management of a Company, but its analytic development is complex. This complexity involves the need to forecast customer behavior well into the future. This behavior includes the timing, frequency, and profitability of a customer's future purchases of products and services. This paper describes a method for computing LTV. First, a multinomial logistic regression provides probabilities for time -of-first-purchase, time-of-second-purchase, etc. for each customer. Then the profits for the first purchase, second purchase, etc. are forecast but only after adjustment for non-purchaser selection bias. Finally, these component models are combined in the LTV formula	logistic distribution, logistic regression, sample, proc step, regression analysis, proc logistic, multinomial logistic regression, conditional logistic regression, proc glmselect, data cleaning, response variable, feature selection, predictor variable, proc reg, proc freq	source	16
219	Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R	Patricia A. Berglund	This presentation emphasizes use of SAS 9.4 to perform multiple imputation of missing data using the `PROC MI` Fully Conditional Specification (FCS) method with subsequent analysis using `PROC SURVEYLOGISTIC` and `PROC MIANALYZE`. The data set used is based on a complex sample design. Therefore, the examples correctly incorporate the complex sample features and weights. The demonstration is then repeated in Stata, IVEware, and R for a comparison of major software applications that are capable of multiple imputation using FCS or equivalent methods and subsequent analysis of imputed data sets based on a complex sample design	imputation, sample, proc step, missing value, multiple imputation, regression analysis, logistic regression, proc mi, proc mianalyze, logistic distribution, proc surveylogistic, estimate, proc calis, nominal variable, random sampling, interval variable, flag variable, confidence interval	source	17
220	Multiple Product Affinity Makes Much More Sense	Hsin-Yi Wang	Retailers proactively seek a data-driven approach to tailor communications or provide customized product recommendations to increase sales and enhance customer loyalty. Product affinity models are often employed to achieve these goals. Often the clustering algorithm assigns customers to groups when the likelihood of purchasing the product type is the highest and the likelihood meets certain minimum and absolute requirements. This does a good job of identifying customers who purchase largely from one product type. However, customers who regularly purchase multiple product types (could be up to 30% of the total universe) are often thrown into a catchall group without a tailored recommendation. This paper addresses how to make a recommendation for this important group of customers with a multiple product affinity model developed in SAS using macros	product affinity, macro definition, proc step, proc freq, proc cluster, proc fastclus	source	12
221	Multiple Ways to Detect Differential Item Functioning in SAS	Yan Zhang	Differential item functioning (DIF), as an assessment tool, has been widely used in quantitative psychology, educational measurement, business management, and insurance and healthcare industries. The purpose of DIF analyses is to detect response differences of items in questionnaires, rating scales, or tests across different subgroups (e.g., gender), while controlling for ability level. There are three general procedures to examine DIF: generalized Mantel-Haenszel (MH), logistic regression, and item response theory (IRT). The SAS system provides flexible and efficient procedures for all these approaches. The goal of this paper is to demonstrate multiple ways to conduct DIF analyses by using different SAS procedures (`PROC FREQ`, LOGISTIC, GENMOD, GLIMMIX, and	logistic regression, logistic distribution, proc step, regression analysis, likelihood function, proc nlmixed, proc glimmix, proc genmod, response variable, odds ratio, sas output, proc logistic, proc freq, proc mixed	source	9
222	Need Additional Statistics in That Report? ODS OUTPUT to the Rescue!	Debbie Buck	You might be familiar with or experienced in writing or running reports using `PROC REPORT`, `PROC TABULATE`, or other methods of report generation. These reporting methods are often very flexible, but they can be limited in the statistics that are available as options for inclusion in the resulting output. SAS provides the capability to produce a variety of statistics through Base SAS and `SAS/STAT` procedures by using `ODS` OUTPUT objects. These procedures include statistics from `PROC CORR`, `PROC FREQ`, and `PROC UNIVARIATE` in Base SAS, as well as `PROC GLM`, `PROC LIFETEST`, `PROC MIXED`, `PROC LOGISTIC`, and `PROC TTEST` in `SAS/STAT`. A number of other procedures can also produce useful `ODS` OUTPUT objects. Commonly requested statistics for reports include p-values, confidence intervals, and test statistics	proc step, proc print, proc report, proc freq, fisher exact test, proc ttest, exact test, dataset, proc compare, sas log, proc contents, sas base, output delivery system, proc means, confidence interval	source	12
223	Nesting Multiple Box Plots and BLOCKPLOTs Using Graph Template Language and Lattice Overlay	Greg Stanek	There are times when the objective is to provide a summary table and graph for several quality improvement measures on a single page to allow leadership to monitor the performance of measures over time. The challenges were to decide which SAS procedure(s) to use and how to integrate multiple SAS procedures to generate a set of plots and summary tables within one page and to determine whether to use box plots or series plots of means or medians. The use of `SGPLOT` and SGPANEL Procedures and Graph Template Language (GTL) were considered. While `SGPLOT` and the SGPANEL procedures provide easier alternatives and better graphics with `ODS` than `GPLOT` and GREPLAY, at times given the nature of the request there is a need to utilize the power of Graph Template Language (GTL) and the `SGRENDER` Procedure, which is the focus	box plot, series plot, proc means, proc step, graph template language, macro definition, confidence interval, macro variable	source	17
224	New Macro Features Added in SAS 9.3 and SAS 9.4	Richard D. Langston	This paper describes the new features added to the macro facility in SAS 9.3 and SAS 9.4. New features described include the /READONLY option for macro variables, the %SYSMACEXIST macro function, the `%PUT` &= feature, and new automatic macro variables such as &SYSTIMEZONEOFFSET	macro variable, macro put, macro facility, put statement, automatic macro variable, macro definition	source	6
225	Next Generation: Using SAS Decision Manager to Modernize and Improve Your Operational Decisions	Steve Sparano	This paper takes you through the steps for ways to modernize your analytical business processes using SAS Decision Manager, a centrally managed, easy-to-use interface designed for business users. See how you can manage your data, business rules, and models, and then combine those components to test and deploy as flexible decisions options within your business processes. Business rules, which usually exist today in SAS code, Java code, SQL scripts, or other types of scripts, can be managed as corporate assets separate from the business process. This will add flexibility and speed for making decisions as policies, customer base, market conditions, or other business requirements change. Your business can adapt quickly and still be compliant with regulatory requirements and support overall process governance and risk. This paper shows how to use SAS Decision Manager to build business rules using a variety of methods including analytical methods and straightforward explicit methods	data validation rules, web service, data governance, big data, data mining, data cleaning	source	20
226	Nifty Uses of SQL Reflexive Join and Subquery in SAS	Cynthia Trinidad	SAS SQL is so powerful that you hardly miss using Oracle PL/SQL. One SAS SQL forte can be found in using the SQL Reflexive Join. Another area of SAS SQL strength is the SQL sub-query concept. The focus of this paper is to show alternative approaches to data reporting and to show how to surface data quality problems using Reflexive Join and Sub-query SQL concepts. The target audience for this paper is the intermediate SAS programmer or the experienced ANSI SQL programmer new to SAS programming	proc sql, create table, proc step	source	5
227	Now That You Have Your Data in Hadoop, How Are You Staging Your Analytical Base Tables?	Steven Sober, Donna De Capite	Well, Hadoop community, now that you have your data in Hadoop, how are you staging your analytical base tables? In my discussions with clients about this, we all agree on one thing: Data sizes stored in Hadoop prevent us from moving that data to a different platform to generate the analytical base tables. To address this problem, I want to introduce to you the SAS In-Database Code Accelerator for Hadoop	by group processing, array, rename statement, macro variable, proc sql, sas base, data cleaning, proc step	source	10
228	Object Oriented Program Design in DS2	Shaun Kaufmann	The DS2 programming language was introduced as part of the SAS 9.4 release. Although this new language introduced many significant advancements, one of the most overlooked features is the addition of object-oriented programming constructs. Specifically, the addition of user-defined packages and methods enables programmers to create their own objects, greatly increasing the opportunity for code reuse and decreasing both development and QA duration. In addition, using this object-oriented approach provides a powerful design methodology where objects closely resemble the real-world entities that they model, leading to programs that are easier to understand and maintain. This paper introduces the object-oriented programming paradigm in a three-step manner. First, the key object-oriented features found in the DS2 language are introduced, and the value each provides is discussed	hash table, sas base, credit scoring, sas programmer	source	16
229	Obtaining a Unique View of a Company: Reports in SAS Visual Analytics	Jacqueline Fraga	The objective of this paper is to present a single report that allows you to view: profitability indicators, results and competition of all company's products, implemented in SAS Visual Analytics in SulAmérica	health insurance, property and casualty	source	15
230	One Check Box to Happiness: Enabling and Analyzing SAS LASR Analytic Server Logs in SAS Visual Analytics	Chris Vincent	EBI administrators who are new to SAS Visual Analytics and used to the logging capability of the SAS OLAP Server might be wondering how they can get their SAS LASR Analytic Server to produce verbose log files. While the SAS LASR Analytic Server logs differ from those produced by the SAS OLAP Server, the SAS LASR Analytic Server log contains information about each request made to LASR tables and can be a great data source for administrators looking to learn more about how their SAS Visual Analytics deployments are being used. This session will discuss how to quickly enable logging for your SAS LASR Analytic Server in SAS Visual Analytics 6.4. You will see what information is available to a SAS administrator in these logs, how they can be parsed into data sets with SAS code, then loaded back into the SAS LASR Analytic Server to create SAS Visual Analytics explorations and reports	sample, sas base, sas administrator	source	5
231	One Report, Many Languages: Using SAS Visual Analytics 7.1 to Localize Your Reports	Will Ballard	Use SAS to communicate with your colleagues and customers anywhere in the world, even if you do not speak the same language! In today's global economy, most of us can no longer assume that everyone in our company has an office in the same building, works in the same country, or speaks the same language. Although it is vital to quickly analyze and report on large amounts of data, we must present our reports in a way that our users can understand. New features in SAS Visual Analytics 7.1 give you the power to generate reports quickly and translate them easily so that your users can compr ehend the results. This paper describes how SAS Visual Analytics Designer 7.1 delivers the Power to Know in the language preferred by the report user!	bar chart, dollar.	source	10
232	Operations Integration, Audits, and Performance Analysis: Getting the Most Out of SAS Environment Manager	Bob Bonham, Bryan Ellington	The SAS Environment Manager Service Architecture expands on the core monitoring capabilities of SAS Environment Manager delivered in SAS 9.4. Multiple sources of data available in the SAS Environment Manager Data Mart - traditional operational performance metrics, events, and ARM, audit, and access logs - together with built-in and custom reports put powerful capabilities in the hands of IT operations. This paper introduces the concept of service- oriented event identification and discusses how to use the new architecture and tools effectively as well as the wealth of data available in the SAS Environment Manager Data Mart. In addition, extensions for importing new data, writing custom reports, instrumenting batch SAS jobs, and leveraging and extending auditing capabilities will be explored	data mart, outlier, proc step, proc means, performance tuning, txt, key performance indicator	source	16
233	Out of Control! A SAS Macro to Recalculate QC Statistics	Jesse M. Pratt	`SAS/QC` provides procedures, such as `PROC SHEWHART`, to produce control charts with centerlines and control limits. When quality improvement initiatives create an out-of-control process of improvement, centerlines and control limits need to be recalculated. While this is not a complicated process, producing many charts with multiple centerline shifts can quickly become difficult. This paper illustrates the use of a macro to efficiently compute centerlines and control limits when one or more recalculations are needed for multiple charts	macro variable, dataset, proc step	source	7
234	Performing Efficient Transposes on Large Teradata Tables Using SQL Explicit Pass-Through	Tao Cheng	It is a common task to reshape your data from long to wide for the purpose of reporting or analytical modeling and `PROC TRANSPOSE` provides a convenient w ay to accomplish this. However, when performing the transpose action on large tables sto red in a database management system (DBMS) such as Teradata, the performance of `PROC TRANSPOSE` can be significantly compromised. In this case, it may be more efficient for the DBMS to perform the transpose task. SAS provides in-database processing technology in `PROC SQL`, which allows the SQL explic it pass-through method to push some or all of the work to the DBMS. This technique has helped facilitate integration between SAS and a wide range of data warehouses and databases, including Teradata, EMC G reenplum, IBM DB2, IBM Netezza, Oracle, and Aster Data. This paper uses the Teradata database as an example DBMS and explains how to transpose a large table that resides in it using the SQL explicit pass-thro ugh method	proc sql, sql pass-through, proc transpose, macro variable, proc step, raw data, teradata, class variable, macro definition, format statement, missing value, sas log, create table, group by, options statement	source	17
235	Permit Me to Permute: A Basic Introduction to Permutation Tests with SAS/IML Software	John Vickery	If your data do not meet the assumptions for a standard parametric test, you might want to consider using a permutation test. By randomly shuffling the data and recalculating a test statistic, a permutation test can calculate the probability of getting a value equal to or more extreme than an observed test statistic. With the power of matrices, vectors, functions, and user-defined modules, the `SAS/IML` language is an excellent option. This paper covers two examples of permutation tests: one for paired data and another for repeated measures analysis of variance. For those new to `SAS/IML` software, this paper of fers a basic introduction and examples of how effective it can be	permutation test, bootstrap, repeated measures, proc iml, proc step, dataset, repeated measures anova, sas base, run, anova, do loop, return, macro variable, call symputx, parametric test, sas function, randomization, sas enterprise guide	source	15
236	Picture-Perfect Graphing with Graph Template Language	Julie VanBuskirk	Do you have reports based on `SAS/GRAPH` procedures, customized with multiple GOPTIONS? Do you dream of those same graphs existing in a GOPTIONS and ANNOTATE free world? Recreating complex graphs using Statistical Graphics (SG) procedures is not only possible, but much easier than you think! Using before and after examples, I will discuss how the graphs were created using the combination of Graph Template Language (GTL) and the SG procedures. This method produces graphs that are nearly indistinguishable from the original. This method simplifies the code required to make complex graphs, allows for maximum re-usability for graphics code, and allows changes to cascade to multiple reports simultaneously	bar chart, horizontal bar chart, proc step	source	10
237	Pin the SAS Tail on the Microsoft Excel Donkey: Automatically Sizing and Positioning SAS Graphics for Excel	Ted Conway	Okay, you've read all the books, manuals, and papers and can produce graphics with `SAS/GRAPH` an d Output Delivery System (`ODS`) Graphics with the best of them. But how do you handle the Final Mile problem -getting your images generated in SAS sized just right and positioned just so in Microsoft `EXCEL`? This paper presents a method of doing so that employs SAS Integration Technologies and `EXCEL` Visual Basic for Applications (VBA) to produce SAS graphics and automatically embed them in `EXCEL` worksheets. This technique might be of interest to all skill levels. It uses Base SAS, `SAS/GRAPH`, `ODS` Graphics, the SAS macro facility, SAS Integration Technologies, Microsoft `EXCEL`, and VBA	excel, proc sgplot	source	5
238	Planning for the Worst--SAS Grid Manager and Disaster Recovery	Glenn Horton, Cheryl Doninger, Doug Haigh	Many companies use geographically dispersed data centers running SAS Grid Manager to provide 24/7 SAS processing capability with the thought that if a disaster takes out one of the data centers, another data center can take over the SAS processing. To accomplish this, careful planning must take into consideration hardware, software, and communication infrastructure along with the SAS workload. This paper looks into some of the options available, focusing on using SAS Grid Manager to manage the disaster workload shift	sas grid, production run	source	5
239	Portfolio Construction with OPTMODEL	Taras Zlupko, Robert Spatz	Investment portfolios and investable indexes determine their holdings according to stated mandate and methodology. Part of that process involves compliance with certain allocation constraints. These constraints are developed internally by portfolio managers and index providers, imposed externally by regulations, or both. An example of the latter is the U.S. Internal Revenu e Code (25/50) concentration constraint, which relates to a regulated investment company (RIC). These codes state that at the end of each quarter of a RIC's tax year, the following constraints should be met: 1) No more than 25 percent of the value of the RIC's assets might be invested in a single issuer. 2) The sum of the weights of all issuers representing more than 5 percent of the total assets should not exceed 50 percent of the fund's total assets	odds ratio, proc step	source	7
240	Practical Applications of SAS Simulation Studio	Edward P. Hughes, Emily Lada	SAS Simulation Studio, a component of `SAS/OR` software for Microsoft Windows environments, provides powerful and versatile capabilities for building, executing, and analyzing discrete -event simulation models in a graphical environment. Its object-oriented, drag-and-drop modeling makes building and working with simulation models accessible to novice users, and its broad range of model configuration options and advanced capabilities makes SAS Simulation Studio suitable also for sophisticated, detailed simulation modeling and analysis. Although the numbe r of modeling blocks in SAS Simulation Studio is small enough to be manageable, the number of ways in which they can be combined and connected is almost limitless. This paper explores some of the modeling methods and constructs that have proven most useful in practical modeling with SAS Simulation Studio	dataset, probability distribution, sample	source	11
241	Practical Implications of Sharing Data: A Primer on Data Privacy, Anonymization, and De-Identification	Gregory S. Barnes Nelson	Researchers, patients, clinicians, and other healthcare industry participants are forging new models for data sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (e.g., EHR, lab, notes), administrative information (claims and billing), social contacts (on-line activity), behavioral trackers (fitness or purchasing patterns), or about contextual (geographic, environmental) or demographic (genomics, proteomics) data, it is clear that as healthcare data proliferates, threats to security grow. Beginning with a review of the major healthcare data breaches in our recent history, this paper highlights some of the lessons that can be gleaned from these incidents	privacy, data protection	source	23
242	Predicting Readmission of Diabetic Patients Using the High-Performance Support Vector Machine Algorithm of SAS Enterprise Miner 13.1	Hephzibah Munnangi, Goutam Chakraborty	Diabetes is a chronic condition affecting people of all ages and is prevalent in around 25.8 million people in the U.S. The objective of this research is to predict the probability of a diabetic patient being readmitted. The results from this research will help hos pitals design a follow -up protocol to ensure that patients having a higher re -admission probability are doing well to p romote a healthy doctor -patient relationship. The data was obtained from the Center for Machine Learning and Intelligent Systems at University of California, Irvine. The data set contains over 100,000 instances and 55 variables such as insulin and length of stay, etc. The data set was split into training and validation to provide an honest assessment of models. Several variable selection te chniques such as Stepwise Regression, Forward Regression, LARS, and LASSO were used. Using LARS, prominent factors were identified in determining the patient readmission rate	missing value, feature selection, support vector machine, data cleaning, likelihood function, regression analysis, proc hpsplit, data mining, machine learning, binary variable, class variable, interval variable, estimate, response variable, nominal variable	source	10
243	Predicting Transformer Lifetime Using Survival Analysis and Modeling Risk Associated with Overloaded Transformers Using SAS Enterprise Miner 12.1	Balamurugan Mohan, Goutam Chakraborty	Utility companies in America are always challenged when it comes to knowing when their infrastructure fails. One of the most critical components of a utility company's infrastru cture is the transformer. It is important to assess the remaining lifetime of transformers so that the company can reduce costs, plan expenditures in advance and mitigate the risk of failure to a large extent. It is also equally important to identify the high risk transformers in advance and maintain them accordingly to avoid sudden loss o f equipment due to overloading. The objective of this paper is to use SAS to predict the lifetime of transformers, identify the various factors that contribute to their failure and model the transformer into high, medium and low risky categories based on load for easy maintenance. The data set used in this study is from a utility company in Southwestern United States and contains around 18,000 observations and 26 variables from 2006 till 2013	regression analysis, hazard function, survival function, proc step, proc phreg, predictor variable, proc hpsplit, survival analysis, censoring, cumulative frequency, probability distribution, kaplan-meier plot, data cleaning, correlation, likelihood function, logistic regression, logistic distribution, response variable	source	18
244	Predictive Modeling Using SAS Visual Statistics: Beyond the Prediction	Xiangxiang Meng, Jennifer Ames, Wayne Thompson	Predictions, including regressions and classifications, are the predominant focus of many statistical and machine-learning models. However, in the era of Big Data, a predictive modeling process contains more than just making the final predictions. For instance, a large collection of data often represents a set of small, heterogeneous populations. Identification of these sub groups is therefore an important step in predictive modeling. Additionally, Big Data data sets are often complex, exhibiting high dimensionality. Consequently, variable selection, transformation, and outlier detection are integral steps. This paper provides working examples of these critical stages using SAS Visual Statistics, including data segmentation (supervised and unsupervised), variable transformation, outlier detection, and filtering, in addition to building the final predictive model using methodology such as linear regressions, decision trees and logistic regressions	proc reg, regression analysis, proc hpsplit, scatter plot, group by, outlier, logistic regression, box plot, score code, bar chart, proc freq, logistic distribution	source	12
245	Preparing Output from Statistical Procedures for Publication, Part 1: PROC REG to APA Format	Vincent DelGobbo, Peter L. Flom	Many scientific and academic journals require that statistical tables be created in a specific format, with one of the most common formats being that of the American Psychological Association (APA). The APA publishes a substantial guide book to writing and formatting papers, including an extensive section on creating tables (Nichol 2010). However, the output generated by SAS statistical procedures does not match this style. This paper discusses techniques to change the SAS procedure output to match the APA guidelines using the SAS Output Delivery System (`ODS`)	proc reg, dataset, proc step, anova, proc print, estimate, output delivery system, confidence interval, macro variable, sas output, footnote statement, regression analysis, sas base, permanent library, proc template, ods rtf	source	27
246	Preparing Students for the Real World with SAS Studio	AnnMaria De Mars	A common complaint of employers is that educational institutions do not prepare students for the types of messy data and multi-faceted requirements that occur on the job. No organization has data that resembles the perfectly scrubbed data sets in the back of a statistics textbook. The objective of the Annual Report Project is to quickly bring new SAS users to a level of competence where they can use real data to meet real business requirements. Many organizations need annual reports for stockholders, funding agencies, or donors. Or, they need annual reports at the department or division level for an internal audience. Being tapped as part of the team creating an annual report used to mean weeks of tedium, poring over columns of numbers in 8-point font in (shudder) `EXCEL` spreadsheets, but no more. No longer painful, using a few SAS procedures and functions, reporting can be easy and, dare I say, fun. All analyses are done using SAS Studio (formerly SAS Web Editor) of SAS OnDemand for Academics. This paper uses an example with actual data for a report prepared to comply with federal grant funding requirements as proof that, yes, it really is that simple.	proc step, proc freq, proc import, proc corr, length statement, character functions, proc means, correlation, frequency analysis, sas studio, attrib statement, character variable, filename statement, index, libname statement, numeric variable, proc gchart, sas log, excel, input statement, dataset	source	18
247	Prioritizing Feeders for Investments: Peformance Analysis Using Data Envelopment Analysis	Victor Henrique de Oliveira, Iguatinan Monteiro	This paper presents a methodology developed to define and prioritize feeders with the least satisfactory performances for continuity of energy supply, in order to obtain an efficiency ranking to support decision - making process regarding the investments to be implemented. The Data Envelopment Analysis (DEA) was the basis for the development, in which was adopted the input oriented model with variable returns to scale. To perform the analysis of the feeders, data from the utility GIS and from the interruption co ntrol system was export to SAS Enterprise Guide, where its manipulation was made possible. Different continuity variables and physical-electrical parameters were consolidated for each feeder for the years of 2011 to 2013. They were separated accordingly to the geographical regions of the concession area, accordingly to their location (urban or rural) and then grouped by the physical similarity	sas enterprise guide, proc freq	source	9
248	Privacy, Transparency, and Quality Improvement in the Era of Big Data and Health Care Reform	Paul Gorrell	The era of big data and health care reform is an exciting and c hallenging time for anyone whose work involves data security, analytics, data visualization, or health services research. This presentation examines important aspects of current approaches to quality improvement in health care based on data transparency and patient choice. We will look at specific initiatives related to the Affordable Care Act (e.g. the qualified entity program of section 10332 which allows the Centers for Medicare and Medicaid Services (CMS) to provide Medicare claims data to organizations for multi-payer quality measurement and reporting, the open payments program, State-level all-payer claims databases to inform improvement and public reporting) within the context of a core issue in the era of big data: security and privacy versus transparency and openness	insurance claims, data visualization, calendar year	source	9
249	PROC CATALOG, the Wish Book SAS Procedure	Louise S. Hadden	SAS data sets have `PROC DATASETS`, and SAS catalogs have `PROC CATALOG`. Find out what the little known `PROC CATALOG` can do for you!	proc step, proc catalog, catalog, proc format, catalog entry, macro catalog	source	6
250	PROC RANK, PROC SQL, PROC FORMAT and PROC GMAP Team Up and a (Map) Legend is Born!	Christianna S. Williams, Louise S. Hadden	The task was to produce a figure legend that gave the quintile ranges of a continuous measure corresponding to each color on a five-color choropleth US map. Actually, we needed to produce the figures and associated legends for several dozen maps for several dozen different continuous measures and time periods as well as associated "alt -text" for compliance with Section 508…so, the process needed to be automated. A method was devised using SAS `PROC RANK` to generate the quintiles, `PROC SQL` to get the data value ranges within each quintile, and `PROC FORMAT` (with the CNTLIN= option) to generate and store the legend labels. The resulting data files and format catalogs are then used to generate both the maps (with legends) and associated "alt text". Then, these processes were rolled into a macro to apply the method for the many different maps and their legends	proc step, proc format, proc sql, proc rank, create table, macro variable, proc report, group by, proc catalog, proc gmap, macro definition, proc means	source	20
251	PROC SQL for PROC SUMMARY Stalwarts	Christianna S. Williams	One of the fascinating features of SAS is that the software often provides multiple ways to accomplish the same task. A perfect example of this is the aggregation and summarization of data across multiple rows or BY groups of interest. These groupings can be study participants, time periods, geographical areas, or really just about any type of discrete classification that one desires. While many SAS programmers may be accustomed to accomplishing these aggregation tasks with `PROC SUMMARY` (or equivalently, `PROC MEANS`), `PROC SQL` can also do a bang -up job of aggregation - often with less code and fewer steps. This step -by-step paper explains how to use PR OC SQL for a variety of summarization and aggregation tasks. It uses a series of concrete, task-oriented examples to do so. For each example, both the `PROC SUMMARY` method and the `PROC SQL` method will be presented, along with discussion of pros and cons of each approach	proc step, proc means, proc sql, group by, missing value, create table, order by, class variable, proc sort, sysdate9, sas programmer, by group processing, raw data, proc freq	source	19
252	Proven Practices for Managing the Enterprise Administrators of a SAS Software Deployment	Robert Collum, Clifford Meyers	There can be a need to provide multiple administrators with the ability to manage your software. The rationale will span from separation of roles and responsibilities (for example, installer and configuration manager) to changing job responsibilities or even just providing coverage while the primary administrator is on vacation. To meet that need, it's tempting to share the logon credentials of your SAS installer account, but doing so can potentially compromise your security and fail a corporate audit. The SAS Installer user ID on a UNIX (or Linux) system is typically named something generic, such as "sas" or "sasinst." That account, like others associated with SAS and other software services, is often not tied to any single person in the enterprise. The role of SAS Administrator could be a job function of one or several people - over time or at any point in time - as professionals follow their careers	audit trail, grid computing	source	9
253	Put Your Data on the Map	Caroline Cutting	A bubble map is a useful tool for identifying trends and visualizing the geographic proximity and intensity of events. This paper illustrates how to use `PROC GEOCODE` and `PROC GMAP` to turn a dataset of addresses and events into a bubble map of the United States with scaled bubbles depicting the location and intensity of events	proc step, proc gmap	source	10
254	Quotes within Quotes: When Single (') and Double (") Quotes are not Enough	Arthur L. Carpenter	Although it does not happen every day, it is not unusual to need to place a quoted string within another quoted string. Fortunately SAS recognizes both single and double quote marks and either can be used within the other. This gives us the ability to have two deep quoting. There are situations, however where two kinds of quotes are not enough. Sometimes we need a third layer or more commonly we need to use a macro variable within the layers of quotes. Macro variables can be especially problematic as they will generally not resolve when they are inside single quotes. However this is SAS, and that implies that there are sev eral things going on at once and that there are several ways to solve these types of quoting problems. The primary goal of this paper is to assist the programmer with solutions to the quotes within quotes problem with special emphasis on the presence of macro variables	macro variable, filename statement, macro facility, &variable, macro quoting function, macro put, macro function	source	6
255	Random vs. Fixed Effects: Which Technique More Effectively Addresses Selection Bias in Observational Studies	Jessica Navratil-Strawn, Stephen Hartley, Ronald J. Ozminkowski	Retrospective case-control designs are frequently used to evaluate healthcare programs when it is not feasible to randomly assign members to participate or not in those programs. Without randomization, estimates of program impact are more susceptible to selection bias because those who participate in an intervention may be different than those who do not, in ways that are difficult to address. This paper will discuss how multinomial propensity score weighing and random effects techniques can be used to reduce the impact selection bias has on observational study outcomes. All results shown are drawn from a return on investment (ROI) analysis using a participant (cases) versus non-participant (control) study design for a fitness reimbursement program (FRP) aiming to reduce healthcare expenditures among participating members	proc mixed, fixed effects, propensity score, covariance, correlation, covariance structure, estimate, response variable, proc step, sensitivity analysis, logistic distribution, randomization, regression analysis, predictor variable	source zip	8
256	Real Time--Is That Your Final Decision?	Diane Hatcher, Jerry Baulier, Steve Sparano	Streaming data is becoming more and more prevalent. Everything is generating data now -social media, machine sensors, and the Internet of Things. And you need to decide what to do with that data right now. And "right now" could mean 10,000 times or more per second. SAS Event Stream Processing provides an infrastructure for capturing streaming data and processing it on the fly-including applying analytics and deciding what to do with that data. All in milliseconds. There are some basic tenets on how SAS provides this extremely high-throughput, low-latency technology to meet whatever streaming analytics your company might want to pursue	stream processing, real-time processing, correlation	source	8
257	Real-Time Risk Aggregation with SAS High-Performance Risk and SAS Event Stream Processing Engine	Albert Hopping, Arvind Kulkarni, Ling Xiang	Risk managers and traders know that some knowledge loses its value quickly. Unfortunately, due to the computationally intensive nature of risk, most risk managers use stale data. Knowing your positions and risk intraday can provide immense value. Imagine knowing the portfolio risk impact of a trade before you execute. This paper shows you a path to doing real-time risk analysis leveraging capabilities from SAS Event Stream Processing and SAS High-Performance Risk. Event Stream Processing offers the ability to process large amounts of data with high throughput and low latency, including streaming real-time trade data from front-office systems into a centralized risk engine. High-Performance Risk enables robust, complex portfolio valuations and risk calculations quickly and accurately. In this paper, we present techniques and demonstrate concepts that enable you to more efficiently use these capabilities together	stream processing, real-time processing, proc step, star schema	source	10
258	Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation	Amber Schmitz, Jessica Navratil-Strawn, Stephen Hartley, Ronald J. Ozminkowski	To stay competitive in the marketplace, healthcare programs must be capable of reporting the true savings to clients. This is a tall order considering most healthcare programs are setup to be available to the client's entire population; thus, the program cannot be conducted as a randomized control trial. In order to evaluate the performance of the program for the client, we use an observational study design which has inherent selection bias due to its inability to randomly assign participants. To reduce the i mpact of bias, we apply propensity score matching to the analysis. This technique is beneficial to healthcare program evaluations because it helps reduce selection bias in the observational analysis and in turn provides a clearer view of the client's savings	propensity score, propensity score matching, correlation, sample, predictor variable, condition index, proc step, sample size, binary variable, regression analysis	source	7
259	Regulatory Compliance Reporting Using SAS XML Mapper	Sarita Kannarath, Phil Hanna, Amitkumar Nakrani, Nishant Sharma	As a part of regulatory compliance requirements, institutions are required to submit reports based on Microsoft `EXCEL`, as per templates supplied by the regulators. This poses several challenges, including the high complexity of templates, the fact that implementation using `ODS` can be cumbersome, and the difficulty in keeping up with regulatory changes and supporting dynamic report content. At the same time, you need the flexibility to customize and schedule these reports as per your business requirements. This paper discusses an approach to building these reports using SAS XML Mapper and the `EXCEL` XML spreadsheet format. This approach provides an easy-to-use framework that can accommodate template changes from the regulators without needing to modify the code. It is implemented using SAS technologies, providing you the flexibility to customize to your needs. This approach also provides easy maintainability	excel, dataset, sample, sas programmer	source	23
260	Regulatory Stress Testing - A Manageable Process with SAS	Wei Chen, John Pechacek, Erik Leaver, Shannon Clark	As a consequence of the financial crisis, banks are required to stress test their balance sheet and earnings based on prescribed macroeconomic scenarios. For example, in the US, this exercise is known as the Comprehensive Capital Analysis and Review (CCAR) or Dodd-Frank Act Stress Testing (DFAST). In order to assess capital adequacy under these stress scenarios, banks need a unified view of their projected balance sheet, incomes, and losses. In addition, the bar for these regulatory stress tests is very high regarding governance and overall infrastructure. Regulators and auditors want to ensure that the granularity and quality of data, model methodology, and assumptions reflect the complexity of the banks. This calls for close internal collaboration and information sharing across business lines, risk management, and finance. Currently, this process is managed in an ad hoc, manual fashion. Results are aggregated from various lines of business using spreadsheets and Microsoft SharePoint	stress testing, model inventory, monte carlo, sas risk, stress scenario, capital planning, macro-economic scenario, sensitivity analysis, sas risk engine, data governance, model governance	source	12
261	Replication Techniques for Variance Approximation	Taylor Lewis	Replication techniques such as the jackknife and the bootstrap have become increasingly popular in recent years, particularly within the field of complex survey data analysis. The premise of these techniques is to treat the data set as if it were the population and repeatedly sample from it in some systematic fashion. From each sample, or replicate, the estimate of interest is computed, and the variability of the estimate from the full data set is approximated by a simple function of the variability amongst the replicate-specific estimates. An appealing feature is that there is generally only one variance formula (per method), regardless the underlying quantity being estimated. The entire process can be efficiently implemented after appending a series of replicate weights to the analysis data set. As will be shown, the SURVEY family of `SAS/STAT` procedures can be exploited to facilitate both the task of appending the replicate weights and approximating variances	sample, bootstrap, degrees of freedom, proc step, proc calis, confidence interval, regression analysis	source	19
262	Reports That Make Decisions: SAS Visual Analytics	Arun Sugumar, Vimal Raj	SAS Visual Analytics provides numerous capabilities to analyze data quickly and efficiently which makes it easier for making key business decisions that are critical for day-to-day operations. Depending on your organization, for example, Human Resources, Sales, or Finance, the data can be easily mined by decision makers, providing information that empowers the user to make key business decisions. The right data preparation during report development is the key to success. SAS Visual Analytics provides the ability to explore the data and to make forecasts using automatic charting capabilities with a simple click-and-choose interface. The ability to load all the historical data into memory enables you to make decisions by analyzing the data patterns. The decision is within reach when the report designer uses SAS Visual Analytics Designer functionality like alerts, display rules, ranks, comments, and others. Planning your data preparation task is critical for the success of the report	data cleaning, sas administrator	source	6
263	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 around HTTP, SAS provides everything you need to make REST calls and to process structured and unstructured data alike. Learn how the `HTTP` procedure and other SAS language features provide everything you need to simply and securely use REST	proc http, rest api, web service	source	12
264	Reversing the IN Operator	Can Tongur	The IN operator within the `DATA STEP` is used for searching a specific variable for some values, either numeric or character (for example, "if X in (2) then..."). This brief note explains how the opposite situation can be managed. That is, how to search for a specific value/-s in multiple variables by applying an ARRAY and the IN operator together	numeric variable, response variable, array, proc print	source	4
265	Risk-Adjusting Provider Performance Utilization Metrics	Stephanie Poley	Pay-for-performance programs are putting increasing pressure on providers to better manage patient utilization through care coordination, with the philosophy that good preventive services and routine care can prevent the need for some high-resource services. Evaluation of provider performance frequently includes measures such as acute care events (ER and inpatient), imaging, an d specialist services, yet rarely are these indicators adjusted for the underlying risk of providers' patient panel. In part, this is because standard patient risk scores are designed to predict costs, not the probability o f specific service utilization. As such, Blue Cross and Blue Shield of North Carolina has developed a methodology to model our members' risk of these events in an effort to ensure that providers are evaluated fairly and to prevent our providers from adverse selection practices	insurance risk score, logistic distribution, regression analysis, binomial distribution, logistic regression, risk adjustment, proc step	source	9
266	Row-Level Security and SAS Visual Analytics	Zuzu Williams	How can you protect your data used by SAS Visual Analytics considering the challenges of data security in today's business environment? SAS has implemented security features in its widely used business intelligence platform, including row-level security in SAS Visual Analytics. Row-level security specifies who can access particular rows in a SAS LASR Analytic Server (LASR) table. Throughout this paper, we discuss two ways-interactively and in batch-of implementing row-level security for LASR tables in SAS Visual Analytics. Both approaches link table-based permission conditions with metadata stored identities	access control, proc step	source	11
267	S-M-U (Set, Merge, and Update) Revisited	Andrew T. Kuligowski	It is a safe assumption that almost every SAS user learns how to use the `SET` statement not long after they're taught the concept of a `DATA STEP`. Further, it would probably be reasonable to guess that almost everyone of those people covered the `MERGE` statement soon afterwards. Many, maybe most, also g ot to try the UPDATE and/or MODIFY statements eventually, as well. It would also be a safe assumption that very few people have taken the time to review the manual since they've learned about those statements. That is most unfortunate, because there are so many options available to the users that can assist them in their tasks of obtaining and combining data sets. This presentation is designed to build onto the basic understanding of SET, MERGE, and UPDATE. It assumes that the attendee or reader has a basic knowledge of those statements, and it introduces various options and usages that extend the utility of these basic commands	dataset, set statement, merge statement, modify statement, update statement, by group processing, libname statement, out=, input statement, proc step, proc append, if-then-else, sysrc, stop, missing value, where statement	source	13
268	Sampling Financial Records Using the SURVEYSELECT Procedure	Roger L. Goodwin	This paper presents an application of the `PROCEDURE SURVEYSELECT` . The objective is to draw a systematic random sample from financial data for review. Topics covered in this paper include a brief review of systematic sampling, variable definitions, serpentine sorting, and an interpretation of the output	sample, systematic sampling	source zip	7
269	Sankey Diagrams in SAS Visual Analytics	Varsha Chawla, Renato Luppi	Before the Internet era, you might not have come across many Sankey diagrams. These diagrams, which contain nodes and links (paths) that cross, intertwine, and have different widths, were named after Captain Sankey. He first created this type of diagram to visualize steam engine efficiency. Sankey diagrams used to have very specialized applications such as mapping out energy, gas, heat, or water distribution and flow, or cost budget flow. These days, it's become very common to display the flow of web traffic or customer actions and reactions through Sankey diagrams. Sankey diagrams in SAS Visual Analytics enable users to easily create meaningful visualizations that represent the flow of data from one event or value to another. In this paper, we consider the components that make up a Sankey diagram: 1. nodes; 2. links; 3. drop off links; and 4. a transaction	proc freq, missing value	source	21
270	SAS Analytics on IBM FlashSystem Storage: Deployment Scenarios and Best Practices	David Gimpl, Matt Key, Narayana Pattipati, Harry Seifert	SAS Analytics enables organizations to tackle complex business problems using Big Data and provide insights needed to make critical business decisions. With actionable and timely insights, organizations can serve customers better, spot market trends sooner and support the launch of new products and offerings. A well-architected enterprise storage infrastructure is needed to realize full potential of SAS analytics. However, as the need for big data analytics and rapid response times increases, the performance gap between server speeds and traditional hard disk drive (HDD) based storage systems can be a significant concern. The growing performance gap can have detrimental effects, particularly when it comes to critical business applications. As a result, organizations are looking for a newer, smarter, faster kind of storage systems to accelerate business insights	business intelligence, grid computing	source	13
271	SAS Analytics plus Warren Buffett's Wisdom Beats Berkshire Hathaway! Huh?	Bruce Bedford	Individual investors face a daunting challenge. They must select a portfolio of securities comprised of a manageable number of individual stocks, bonds and/or mutual funds. An investor might initiate their portfolio selection process by choosing the number of unique securities to hold in their portfolio. This is both a practical matter and a matter of risk management. It is practical because there are tens of thousands of actively traded securities from which to choose, and it is impractical for an individual investor to own every available security. It is also a risk management measure because investible securities bring with them the potential of financial loss - to the point of becoming valueless in some cases. Increasing the number of securities in a portfolio decreases the probability that an investor will suffer drastically from corporate bankruptcy, for instance. However, holding too many securities in a portfolio can restrict performance	monte carlo, correlation, sample, proc step, histogram, data cleaning, time series analysis, interquartile range, pearson correlation, proc autoreg, numeric functions, unit root, validation set, anova	source	20
272	SAS and Hadoop. 4th Annual State of the Union	Paul Kent	Presentation	proc sql, libname statement, proc step, group by, proc means, set statement, data cleaning	source	70
273	SAS and SAP Business Warehouse on SAP HANA--What's in the Handshake?	Tatyana Petrova	Is your company using or considering using SAP Business Warehouse (BW) powered by SAP HANA? SAS provides various levels of integration with SAP BW in an SAP HANA environment. This integration enables you to not only access SAP BW components from SAS, but also to push portions of SAS analysis directly into SAP HANA, accelerating predictive modeling and data mining operati ons. This paper explains the SAS toolset for different integration scenarios, highlights the newest technologies contributing to integration, and walks you through examples of using SAS with SAP BW on SAP HANA. The paper is targeted at SAS and SAP developers and architects interested in building a productive analytical environment with the help of the latest SAS and SAP collaborative advancements	data mining, sas model manager, high-performance sas, proc sql, champion model, dimension table, in-database processing, sample, sas base, credit card, predictive analytics, score code, dataset, group by, implicit sql pass-through, training set, proc step, sas enterprise guide	source	17
274	SAS Certification as a Tool for Professional Development	Andra Northup, Susan J. Slaughter	In today's competitive job market, both recent graduates and experienced professionals are looking for ways to set themselves apart from the crowd. SAS certification is one way to do that. SAS Institute Inc. offers a range of exams to validate your knowledge level. In writing this paper, we have drawn upon our personal experiences, remarks shared by new and longtime SAS users, and conversations with experts at SAS. We discuss what certification is and why you might want to pursue it. Then we share practical tips you can use to prepare for an exam and do your best on exam day. All information is accurate as of March 2015	sas consultant, sas 9, sas base, sas programmer, raw data, input statement, dataset, date variable, proc freq	source	17
275	SAS Code for Making Microsoft Excel Files Section 508 Compliant	Christopher J. Boniface	Can you create hundreds of great looking `EXCEL` tables all within SAS and make them all Section 508 compliant at the same time? This paper will examine how to use `ODS` `TAGSETS` EXCELXP and other BASE SAS features to create fantastic looking `EXCEL` worksheet tables that are all Section 508 compliant. This paper will demonstrate that there is no need for any outside intervention or pre or post-meddling with the `EXCEL` files to make them Section 508 compliant. We do all with simple BASE SAS code	excel, sas base, proc format, proc step, macro loop	source	5
276	SAS Data Management: Technology Options for Ensuring a Quality Journey Through the Data Management Process	Mark Craver	When planning for a journey, one of the main goals is to get the best value possible. The same can be said for your corporate data as it journeys through the data management process. It is your goal to get the best data in the hands of decision makers in a timely fashion, with the lowest cost of ownership and the minimum number of obstacles. The SAS Data Management suite of products provides you with many options for increasing the value of your data throughout the data management process. The purpose of this session is to focus on how the SAS Data Management solution can be used to ensure the delivery of quality data, in the right format, to the right people, at the right time. The journey is yours, the technology is ours-together we can make it a fulfilling and rewarding experience	data cleaning, sas programmer, raw data, batch job, sas enterprise guide	source	12
277	SAS Does Data Science: How to Succeed in a Data Science Competition	Patrick Hall	First introduced in 2013, the Cloudera Data Science Challenge is a rigorous competition in which candidates must provide a solution to a real-world big data problem that surpasses a benchmark specified by some of the world's elite data scientists. The Cloudera Data Science Challenge 2 (in 2014) involved detecting anomalies in the United States Medicare insurance system. Finding anomalous patients, procedures, providers, and regions in the competition's large, complex, and intertwined data sets required industrial-strength tools for data wrangling and machine learning. This paper shows how I did it with SAS	data mining, data science, sas base, euclidean distance, cluster analysis, outlier, regression analysis, data cleaning, macro definition, neural network, sample, proc fastclus, dataset, proc freq	source zip	11
278	SAS Enterprise Guide 5.1 and PROC GPLOT--the Power, the Glory and the PROC-tical Limitations	Christopher Klekar, Gabriela Cantu	Customer expectations are set high when Microsoft `EXCEL` and PowerPoint are used to design reports. Using SAS for reporting has benefits because it generates plots directly from prepared datasets, automates the plotting process, minimizes labor intensive manual construction using Microsoft products and does not compromise the presentation value. SAS Enterprise Guide 5.1 (EG 5.1) has a powerful point and click method that is quick and easy to use; however, it is limited in its ability to customize the output to mimic manually created Microsoft graphics. This paper will demonstrate how EG is the perfect starting point to create initial code for plots using the `SAS/GRAPH` point-and-click features and how the code can be enhanced using established `PROC GPLOT`, ANNOTATE, and `ODS` options to recreate the look and feel of `EXCEL` and PowerPoint generated plots. Examples will show the generation of plots and tables using `PROC TABULATE` to embed the plot data into the graphical output	line chart, proc step, macro variable, proc gplot, dataset, sas enterprise guide, proc tabulate, excel, sas output, proc sql, macro definition	source	16
279	SAS Enterprise Guide or SAS Studio: Which is Best for You?	Philip R. Holland	SAS Studio (previous known as SAS Web Editor) was introduced in SAS 9.4 M1 as an alternative programming environment to Enterprise Guide (EG) and interactive SAS (DMS). SAS Studio is different in many ways to EG and DMS. As a programmer I currently use EG to help me code, test, maintain and organize my SAS programs. I have interactive SAS installed on my PC, but I still prefer to write my programs in EG, because I know it will save my log and output whenever I run a program, even if that program crashes and takes the SAS session with it! So should I now be using SAS Studio instead, and should you be using it too?	sas studio, sas enterprise guide, sas programming, sas library, sas syntax, horizontal bar chart, bar chart, data analyst	source	23
280	SAS Enterprise Guide System Design	Steven J. First, Jennifer First-Kluge	A good system should embody the following characteristics: It is planned, maintainable, flexible, simple, accurate, restartable, reliable, reusable, automated, documented, efficient, modular, and validated. This is true of any system, but how to implement this in SAS Enterprise Guide is a unique endeavor. We will provide a brief overview of these characteristics and then dive deeper into how an Enterprise Guide user should approach developing both ad hoc and production systems	sas enterprise guide, proc sql, sas enterprise guide project, proc step, dataset, excel	source	12
281	SAS for Six Sigma - An Introduction	Daniel R. Bretheim	Six Sigma is a business management strategy that seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified financial targets. All Six Sigma project methodologies include an extensive analysis phase in which SAS software can be applied. JMP software is widely used for Six Sigma projects. However, this paper demonstrates how Base SAS (and a bit of `SAS/GRAPH` and `SAS/STAT` software) can be used to address a wide variety of Six Sigma analysis tasks. The reader is assumed to have a basic knowledge of Six Sigma methodology. Therefore, the focus of the paper is the use of SAS code to produce outputs for analysis	null hypothesis, standard deviation, anova, sample, correlation, box plot, proc freq, histogram	source	32
282	SAS Formats Top Ten	Christianna S. Williams	SAS formats can be used in so many different ways! Even the most basic SAS format use -modifying the way a SAS data value is displayed without changing the underlyi ng data value - holds a variety of nifty tricks, such as nesting formats, formats that affect various style attributes and conditional formatting. Add in picture formats, multi-label formats, using formats for data cleaning, and formats for joins and table look-ups, and we have quite a bag of tricks for the humble SAS format and `PROC FORMAT` , which is used to generate them. This paper describes two handfuls of very useful programming techniques that employ SAS formats. While this paper will be appropriat e for the newest SAS user, it will also focus on some of the lesser - known features of formats and `PROC FORMAT` and so should be useful for even quite experienced users of SAS	proc step, proc format, proc tabulate, format statement, missing value, char., class variable, proc catalog, libname statement, ods rtf, user-defined format, data cleaning	source	18
283	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
284	SAS Model Manager: An Easy Method for Deploying SAS Analytical Models into Relational Databases and Hadoop	Jifa Wei, Kristen Aponte	SAS Model Manager provides an easy way to deploy analytical models into various relational databases or into Hadoop using either scoring functions or the SAS Embedded Process publish methods. This paper gives a brief introduction of both the SAS Model Manager publishing functionality and the SAS Scoring Accelerator. It describes the major differences between using scoring functions and the SAS Embedded Process publish methods to publish a model. The paper also explains how to perform in - database processing of a published model by using SAS applications as well as SQL code outside of SAS. In addition to Hadoop, SAS also supports these databases: Teradata, Oracle, Netezza, DB2, and SAP HANA. Examples are provided for publishing a model to a Teradata database a nd to Hadoop. After reading this paper, you should feel comfortable using a published model in your business environment	sas model manager, proc sql, sample, score code, teradata, challenger model, macro definition, dataset, champion model, proc step	source	12
285	SAS University Edition--Connecting SAS Software in New Ways to Build the Next Generation of SAS Users	Polly Mitchell-Guthrie, Amy Peters	Are you a SAS software user hoping to convince your organization to move to the latest product release? Has your management team asked how your organization can hire new SAS users familiar with the latest and greatest procedures and techniques? SAS Studio and SAS University Edition might provide the answers for you. SAS University Edition was created for teaching and learning. It's a new downloadable package of selected SAS products (Base SAS, `SAS/STAT`, `SAS/IML`, `SAS/ACCESS` Interface to PC Files, and SAS Studio) that runs on Windows, Linux, and Mac. With the exploding demand for analytical talent, SAS launched this package to grow even more SAS users. Part of the way SAS is helping grow that base of users is through the interface to SAS University Edition: SAS Studio. SAS Studio is a developmental web application for SAS that you access through your web browser and-since the first maintenance release of SAS 9.4-is included in Base SAS at no additional charge	sas studio, sas base, sas enterprise guide, proc sgplot	source	9
286	SAS vApps 101	Gary Kohan, Danny Hamrick, Connie Robison, Rob Stephens, Peter Villiers	SAS vApps (virtual applications) are designed to both logically and physically encapsulate a single- or multi-tier software solution into one or more virtual machines. In this paper, we examine the conceptual, logical, and physical design perspectives of a SAS vApp to give a high-level understanding of both the technical and business benefits of SAS vApp, and of the design decisions in envisioning and constructing a particular SAS vApp. These perspectives are presented in the context of the user roles involved in the lifecycle of a SAS vApp	business intelligence, data governance	source	10
287	SAS Visual Analytics Environment Stood Up? Check! Data Automatically Loaded and Refreshed? Not Quite	Jason Shoffner, Brandon Kirk	Once you have a SAS Visual Analytics environment up and running, the next important piece to the puzzle is to keep your users happy by keeping their data loaded and refreshed on a consistent basis. Loading data from the SAS Visual Analytics user interface is a great first start and great for ad hoc data exploring. But automating this data load so that users can focus on exploring the data and creating reports is where the power of SAS Visual Analytics comes into play. By using tried-and-true SAS Data Integration Studio techniques (both out of the box and custom transforms), you can easily make this happen. Proven techniques such as "sweeping" from a source library and "stacking" similar HDFS tables into SAS LASR Analytic Server for consumption by SAS Visual Analytics are presented using SAS Visual Analytics and SAS Data Integration Studio	libname statement, filename statement	source	7
288	SAS Visual Analytics for Fun and Profit: A College Football Case Study	John Davis	SAS Visual Analytics is a powerful tool for exploring, analyzing, and reporting on your data. Whether you understand your data well or are in need of additional insights, SAS Visual Analytics has the capabilities you need to discover trends, see relationships, and share the results with your information consumers. This paper presents a case study applying the capabilities of SAS Visual Analytics to NCAA Division I college football data from 2005 through 2014. It follows the process from reading raw comma -separated values (`CSV`) files through processing that data into SAS data sets, doing data enrichment, and finally loading the data into in-memory SAS LASR tables. The case study then demonstrates using SAS Visual Analytics to explore detailed play-by-play data to discover trends and relationships, as well as to analyze team tendencies to develop game-time strategies	raw data, proc hpsplit, correlation	source	18
289	SAS Visual Analytics: Emerging Trend in Institutional Research	Sivakumar Jaganathan, Thulasi Kumar Raghuraman	Institutional research and effectiveness offices at most institutions are often the primary beneficiaries of the data warehouse (DW) technologies. However, at many institutions, building the data warehouse for growing accountability, decision support, and the institutional effectiveness needs are still unfulfilled, in part due to the growing data volumes as well as the prohibitively expensive data warehousing costs built by UIT departments. In recent years, many institutional research offices in the country are often asked to take a leadership role in building the DW or partner with the campus IT department to improve the efficiency and effectiveness of the DW development	business intelligence, data warehouse, sas enterprise guide, excel	source	9
290	SAS Visual Analytics: Supercharging Data Reporting and Analytics	Ryan Marcum	Presentation	sas visual analytics, analytics platform	source	4
291	SAS Visual Analytics: The Value in Leveraging Your Preexisting Data Sets	Shaun Kaufmann	As a risk management unit, our team has invested countless hours in developing the processes and infrastructure necessary to produce large, multipurpose analytical base tables. These tables serve as the source for our key reporting and loss provisioning activities, the output of which (reports and GL bookings) is disseminated throughout the corporation. Invariably, questions arise and further insight is desired. Traditionally, any inquiries were returned to the original analyst for further investigation. But what if there was a way for the less technical user base to gain insights independently? Now there is with SAS Visual Analytics. SAS Visual Analytics is often thought of as a big data tool, and while it is certainly capable in this space, its usefulness in regard to leveraging the value in your existing datasets should not be overlooked	data cleaning, proc freq	source	6
292	SAS Workshop: Data Mining	Chip Wells	Presentation		source	10
293	SAS Workshop: Forecasting	Catherine Truxillo, George C. J. Fernandez, Terry J. Woodfield	Presentation		source	52
294	SAS Workshop: SAS Data Loader for Hadoop	Kari Richardson	Presentation		source	46
295	SAS Workshop: SAS Visual Analytics	Nicole Ball	Presentation		source	40
296	SAS Workshop: SAS Visual Statistics 7.1	Catherine Truxillo, Xiangxiang Meng, Mike Jenista	Presentation		source	20
297	SAS Workshop: Statistical Analysis with SAS University Edition and SAS Studio	Danny R. Modlin	Presentation		source	56
298	SAS Workshop: Text Analytics	Terry J. Woodfield	Presentation		source	50
299	SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data	Maura E. Stokes, Bob Rodriguez	The latest release of `SAS/STAT` software brings you powerful techniques that will make a difference in your work, whether your data are massive, missing, or somewhere in the middle. New imputation software for survey data adds to an expansive array of methods in `SAS/STAT` for handling missing data, as does the production version of the `GEE` procedure, which provides the weighted generalized estimating equation approach for longitudinal studies with dropouts. An improved quadrature method in the GLIMMIX `PROCEDURE MEANS` you can fit models not previously possible. The `HPSPLIT` procedure provides a rich set of methods for statistical modeling with classification and regression trees, including cross validation and graphical displays. The `HPGENSELECT` procedure adds support for LASSO model selection for generalized linear models. And new software implements generalized additive models by using an approach that handles large data easily	proc step, imputation, proc mixed, likelihood function, regression analysis, logistic distribution, proc gam, bayesian statistics, logistic regression, proc logistic, proc hpsplit, proc glimmix, binomial distribution	source	25
300	SASTRACE: Your Key to RDBMS Empowerment	Andrew Howell	For the many relational databases supported by SAS/ ACCESS products (Oracle, Teradata, DB2, MySQL, SQL Server, Hadoop, Greenplum, PC Files, to name but a few), there are a myriad of RDBMS-specific options at your disposal, but how do you know the right options for any given situation ? How much data should you transfer at a time? Which SAS functions can be passed through to the database? Which cannot? How do you verify your processes are running effic iently? How do you test and validate any changes? The answer lies with the feedback capabilities of the `SASTRACE` system option	sas log, proc sql, structured data, relational database, sample, cpu time, data standard, macro call, sas programmer, dataset, where statement, options statement	source	12
301	Selection and Transformation of Continuous Predictors for Logistic Regression	Bruce S. Lund	This paper discusses the selection and transformation of continuous predictor variables for the fitt ing of binary logistic models. The paper has two parts: (1) A procedure and associated SAS macro is presented which can screen hundreds of predictor variables and 10 transformations of these variables to determine their predictive power for a logistic regression. The SAS macro passes the training data set twice to prepare the transformations and one more time through `PROC TTEST`. (2) The FSP (function selection procedure) and a SAS implementation of FSP are discussed. The FSP tests all transformations from among a class of "FSP transformations" and finds the one with maximum likelihood when fitting the binary target. Royston and Sauerbrei in a 2008 book have popularized the FSP	logistic distribution, logistic regression, discriminant analysis, regression analysis, sample, likelihood function, macro definition, proc step, proc logistic, maximum likelihood estimation, predictor variable, normal distribution, hypothesis testing, type ii error, null hypothesis, proc ttest, standard deviation, histogram	source	16
302	Sending Text Messages to your Phone via the DATA Step	Matthew T. Slaughter	Text messages (SMS) are a convenient way to receive notifications away from your computer screen. In SAS, text messages can be sent to mobile phones via the `DATA STEP`. This paper briefly describes several methods for sending text messages from SAS and explores possible applications	filename statement, put statement	source	4
303	Sensing Demand Signals and Shaping Future Demand Using Multi-tiered Causal Analysis	Charlie Chase	The two primary objectives of multi-tiered casual analysis (MTCA) are to support and evaluate business strategies that are based on the effectiveness of sales and marketing actions in both a competitive and holistic environment. By tying the downstream data-point-of-sale (POS) or syndicated scanner data- and the performance of a market, channel, brand, product, or SKU (or some combination) at retail to internal upstream replenishment shipments (or sales orders) across time, you can simulate and evaluate the outcome of making a change to sales and marketing programs (demand) in order to determine the full impact on supply (shipments). MTCA enables you to capture the entire supply chain by using predictive analytics to sense sales and marketing strategies, and then use "What If" scenario analysis to shape future demand. Finally, by linking future shaped demand to supply (shipments), you can arrive at a most realistic supply response (shipment forecast)	predictor variable, sample, demand forecasting, causal inference	source	8
304	Show Me the Money! Text Analytics for Decision-Making in Government Spending	Tom Sabo	Understanding organizational trends in spending can help overseeing government agencies make appropriate modifications in spending to best serve the organization and the citizenry. However, given millions of line items for organizations annually, including free-form text, it is unrealistic for these overseeing agencies to succeed by using only a manual approach to this textual data. Using a publicly available data set, this paper explores how business users can apply text analytics using SAS Contextual Analysis to assess trends in spending for particular agencies, apply subject matter expertise to refine these trends into a taxonomy, and ultimately, categorize the spending for organizations in a flexible, user-friendly manner. SAS Visual Analytics enables dynamic exploration, including modeling results from SAS Visual Statistics, in order to assess areas of potentially extraneous spending, providing actionable information to the decision makers	unstructured data, correlation, sas enterprise guide, nominal variable, dataset, proc hpsplit, binary variable, data cleaning	source	13
305	Snapshot SNAFU: Preventative Measures to Safeguard Deliveries	Spencer Childress, Alexandra Buck	Little did you know that your last delivery ran on incomplete data. To make matters worse, the client realized the issue first. Sounds like a horror story, no? A few preventative measures can go a long way in ensuring that your data are up-to-date and progressing normally. At the data set level, metadata comparisons between the current and previous data cuts will help identify observation and variable discrepancies. Comparisons will also uncover attribute differences at the variable level. At the subject level they will identify missing subjects. By compiling these comparison results into a comprehensive scheduled e-mail, a data facilitator need only skim the report to confirm that the data is good to go --or in need of some corrective action. This paper introduces a suite of checks contained in a macro that will compare data cuts at the dataset, variable, and subject levels and produce an e-mail report	proc sql, dataset, proc step, data step, macro definition	source zip	6
306	Social Media and Open Data Integration through SAS Visual Analytics and SAS Text Analytics for Public Health Surveillance	Manuel Figallo-Monge, Emily McRae	A leading killer in the United States is smoking and, moreover, over 8.6 million Americans live with a serious illness caused by sm oking or second -hand smoking. Despite this, over 46.6 m illion U.S. adults smoke cigarettes, cigars, and pipes all of which have been linked to ch ronic diseases. What about e-cigarettes - how might they affect this public health situation, particularly among each of the 50 United States? In other words, c an monitoring public opinions of e -cigarettes using open data (such as Twitter ), SAS Visual Analytics, and SAS Text Analytics help inform insights on the potential dangers of these new products? To what extent can social media provide lead generation for anti -smoking campaigns, prioritization of surveys, and early detection of e-cigarette outcomes? These are some of many analytic questions which may arise in coming years, as more data pertaining to e-cigarettes becomes available	correlation, sas enterprise guide, unstructured data, regression analysis, proc freq	source	19
307	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
308	Someone Changed My SAS Visual Analytics Report! How an Automated Version Control Process Can Rescue Your Report and Save Your Sanity	Jerry J. Hosking	Your enterprise SAS Visual Analytics implementation is on its way to being adopted throughout your organization, unleashing the production of critical business content by business analysts, data scientists, and decision makers from many business units. This content is relied upon to inform decisions and provide insight into the results of those decisions. With the development of SAS Visual Analytics content decentralized into the hands of business users, the use of automated version control is essential to providing protection and recovery in the event of inadvertent changes to that content. Re -creation of complex report objects accidentally modified by a business user is time-consuming and can be eliminated by maintaining a version control repository of report (and other) objects created in SAS Visual Analytics. This paper walks through the steps for implementing an automated process for version control using SAS	sample, spk file, error message, macro call, proc sql	source	18
309	Something for Nothing? Adding Group Descriptive Statistics Using SAS PROC SQL Subqueries II	Sunil Kumar Gupta	Can you actually get something for nothing? With the SAS `PROC SQL` subquery and remerging features, yes, you can. When working with categorical variables, you often need to add group descriptive statistics such as group counts and minimum and maximum values for further BY -group processing. Instead of first creating the group coun t and minimum or maximum values and then merging the summarized data set to the original data set, why not take advantage of `PROC SQL` to complete two steps in one? With the `PROC SQL` subquery and summary functions by the group variable, you can easily remerge the new group descriptive statistics with the original data set. Now with a few `DATA STEP` enhancements, you too can include percent calculations	proc sql, proc means, proc step, group by, class variable, create table, by group processing, numeric variable	source	10
310	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
311	SSL Configuration Best Practices for SAS Visual Analytics 7.1 Web Applications and SAS LASR Authorization Service	Heesun Park, Jerome Hughes	One of the challenges in Secure Socket Layer (SSL) configuration for any web configuration is the SSL certificate management for client and server side. The SSL overview covers the structure of the x.509 certificate and SSL handshake process for the client and server components. There are three distinctive SSL client/server combinations within the SAS Visual Analytics 7.1 web application configuration. The most common one is the browser accessing the web application. The second one is the internal SAS web application accessing another SAS web application. The third one is a SAS Workspace Server executing a `PROC` or `LIBNAME` statement that accesses the SAS LASR Authorization Service web application. Each SSL client/server scenario in the configuration is explained in terms of SSL handshake and certificate arrangement. Server identity certificate generation using Microsoft Active Directory Certificate Services (ADCS) for enterprise level organization is showcased	libname statement, proc step	source	10
312	Standardizing the Standardization Process	Avery Long, Frank A. Ferriola	A prevalent problem surrounding ETL development is the ability to apply consistent logic/manipulation of source data when migrating to target data structures. Certain inconsistencies that add a layer of complexity include, but are not limited to, naming conventions and data types associated to multiple sources, numerous solutions applied by an array of developers, and mu ltiple points of updating. In this paper, we examine the evolution of implementing a best practices solution during the process of data delivery, with respect to standardizing data. The solution begins with injecting the transformations of the data directly into the code at the standardized layer via Base SAS or SAS Enterprise Guide . A more robust method that we will explore is to apply these transformations with SAS Macro Facility . This provides the capability to apply these changes in a consistent manner across multiple sources	sas base, create table, data migration, macro definition, proc sql, financial services, proc step, macro facility	source	13
313	Statistical Analysis of Publicly Released Survey Data with SAS/STAT Software SURVEY Procedures	Donna Brogan	Several U.S. federal agencies conduct national surveys to monitor health status of residents. Many of these agencies release their survey data to the public. Investigators might be able to address their research objectives by conducting secondary statistical analyses with these available data sources. This paper describes the steps in using the `SAS/STAT` SURVEY procedures to analyze publicly released data from surveys that use probability sampling to make statistical inference to a carefully defined population of elements (the target population)	sample, population, proc step, proc surveyfreq, parameter, random sampling, proc calis, sample weight, null hypothesis, proc surveymeans, cluster sampling, chi-square test, confidence interval, data science, odds ratio, sample size, missing value, proc surveyreg, nominal variable, interval variable, data analyst, response variable, imputation, dataset, proc freq	source	20
314	Statistical Evaluation of the Doughnut Clustering Method for Product Affinity Segmentation	Darius S. Baer, Goutam Chakraborty	Product affinity seg mentation is a powerful technique for marketers and sales professionals to gain a good understanding of customers' needs, preferences, and purchase behavior. Performing product affinity segmentation is quite challenging in practice because product level da ta usually have high skewness, high kurtosis, and large percentage of zero values. The Doughnut clustering method has been shown to be effective using rea l data, and was presented at SAS Global Forum 2013 (Baer & Chakraborty, 2013) . However, the Doughnut clustering method is not a panacea for addressing the product affinity segmentation problem. There is a clear need for a comprehensive evaluation of this method in order to be able to develop generic guidelines for practitioners on when to apply the method. In this paper, we meet the need by evaluating the Doughnut clustering method on simulated data with different levels of skewness, kurtosis, and percentage of zero values	product affinity, standard deviation, market basket analysis, correlation, chi-square test, transaction data	source zip	12
315	Staying Relevant in a Competitive World: Using the SAS Output Delivery System to Enhance, Customize, and Render Reports	Chevell T. Parker	Technology is always changing. To succeed in this ever-evolving landscape, organizations must embrace the change and look for ways to use it to their advantage. Even standard business tasks such as creating reports are affected by the rapid pace of technology. Reports are key to organizations and their customers. Therefore, it is imperative that organizations employ current technology to provide data in customized and meaningful reports across a variety of media. The SAS Output Delivery System (`ODS`) gives you that edge by providing tools that enable you to package, present, and deliver report data in more meaningful ways, across the most popular desktop and mobile devices. To begin, the paper illustrates how to modify styles in your reports using the `ODS` CSS style engine, which incorporates the use of cascading style sheets (CSS) and the `ODS` document object model (DOM). You also learn how you can use `ODS` to customize and generate reports in the body of email messages	excel, proc step, ods html, filename statement, output delivery system, by group processing, macro function, ods excel, proc template, proc sql, dosubl, proc print, options statement	source	26
316	Step into the Cloud: Ways to Connect to Amazon Redshift with SAS/ACCESS	James Wang, Salman Maher	Every day companies all over the world are moving their data into the cloud. While many options are 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 to help you decide which is best for you. Topics that we discuss here are building a connection, choosing appropriate data types, and SQL functions	amazon redshift, proc sql, amazon s3, dataset, libname statement, bufsize, sas function, sql pass-through, proc step	source	13
317	Strategies for Error Handling and Program Control: Concepts	Thomas E. Billings	SAS provides a complex ecosystem with multiple tools and products that run in a variety of environments and modes. The SAS system provides numerous error-handling and program control statements, options, and features. These features can function differently according to the product/run environment, and many of them have constraints and limitations. Here we review a n umber of potential error handling and program control strategies that can be employed, along with some of the inherent limitations of each. The bottom line is that there is no single strategy that will work in all environments, all products, and all run modes. Instead, programmers need to consider the underlying program requirements and choose the optimal strategy for their situation	sas enterprise guide, exception handling, sas base, proc step, macro variable, error, proc sql	source zip	9
318	Strengthening Diverse Retail Business Processes with Forecasting: Practical Application of Forecasting Across the Retail Enterprise	Alex Chien, Elizabeth Cubbage, Wanda Shive	In today's omni-channel world, consumers expect retailers to deliver the product they want, where they want it, when they want it, at a price they accept. A major challenge many retailers face in delighting their customers is successfully predicting consumer demand. Business decisions across the enterprise are affected by these demand estimates. Forecasts used to inform high-level strategic planning, merchandising decisions (planning assortments, buying products, and pricing, allocation and replenishment of inventory) and operational execution (labor planning) are similar in many respects. However, each business process requires careful consideration of specific input data, modeling strategies, and output requirements. Learn how leading retailers are increasing sales and profitability by operationalizing forecasts that improve decisions across their enterprise	price elasticity, time series analysis, regression analysis, stage 1, residual, forecast horizon, stage 2	source	21
319	Stress Testing for Mid-Sized Banks	Charyn Faenza	In 2014, for the first time, mid-market banks (consisting of banks and bank holding companies with $10- $50 billion in consolidated assets) were required to submit Capital Stress Tests to the federal regulators under the Dodd-Frank Act Stress Testing (DFAST). This is a process large banks have been going through since 2011. However, mid-market banks are not positioned to commit as many resources to their annual stress tests as their largest peers. Limited human and technical resources, incomplete or non- existent detailed historical data, lack of enterprise-wide cross-functional analytics teams, and limited exposure to rigorous model validations are all challenges mid-market banks face. While there are fewer deliverables required from the DFAST banks, the scrutiny the regulators are placing on the analytical modes is just as high as their expectations for Comprehensive Capital Analysis and Review (CCAR) banks	stress testing, data cleaning, model validation	source	11
320	Structuring your SAS Applications for Long-Term Survival: Reproducible Methods in Base SAS Programming	Paul Thomas	SAS users organize their applications in a variety of ways. However, there are some approaches that are more successful, and some that are less successful. In particular, the need to process some of the code some of the time in a file is sometimes challenging. Reproducible research methods require that SAS applications be understandable by the author and other staff members . Structuring your SAS analyses can either help or hinder the processes of data access, data analysis, and data presentation. These tasks must be compartmentalized. This can be done using a well-defined program. The author presents his structuring algorithm, and discusses the characteristics of good structuring methods for SAS applications. Reproducible research methods are becoming more c entrally important, and SAS users must keep up with the current developments	proc step, macro variable, macro loop, proc import, proc sort, proc means, by group processing, sas library, proc format, data science, macro catalog, proc sgplot, %if-%then-%else, excel, macro definition	source zip	13
321	Sums of Squares: The Basics and a Surprise	Sheila Barron, Michelle Mengeling	Most Design of Experiments textbooks cover Type I, Type II, and Type III sums of squares, but many researchers and statisticians fall into the habit of using one type mindlessly. This Breakout Session will review sum of squares basics, illustrate the importance of choosing the appropriate sum of squares type, and highlight differences in `PROC GLM` and `PROC REG` parameterization coding	proc step, proc reg, proc glm, proc genmod, regression analysis, main effect, anova, interaction effect, multiple regression, binary variable, nominal variable, response variable, sample size, sample, predictor variable	source	8
322	Survey Sample Designs for Auditing, Fraud, and Forensics.	Turner Bond	Sampling for audits and forensics presents special challenges: Each survey/sample item requires examination by a team of professionals, so sample size must be contained. Surveys involve estimating-- not hypothesis testing. So "power" is not a helpful concept. Stratification and modeling is often required to keep sampling distributions from being skewed. A precision of alpha is not required to create a confidence interval of 1-alpha, but how small a sample is supportable? Many times replicated sampling is required to prove the applicability of the design. Given the robust, programming-oriented approach of SAS, the random selection, stratification, and optimizing techniques built into SAS can be used to bring transparency and reliability to the sample design process	sample, confidence interval, sample size, probability distribution, weibull distribution, proc step, hypothesis testing, normal distribution, proc calis, outlier, proc surveyselect, macro definition, macro loop, proc sort	source	13
323	Survival Analysis with Survey Data	Joseph C. Gardiner	Surveys are designed to elicit information on population characteristics. A survey design typically combines stratification and multistage sampling of intact clusters, sub-clusters and individual units with specified probabilities of selection. A survey sample can produce valid and reliable estimates of population parameters at a fraction of the cost of carrying out a census of the entire population, with clear logistical efficiencies. For analyses of survey data SAS software provides a suite of procedures from SURVEYMEANS and SURVEYFREQ for generating descriptive statistics and conducting inference on means and proportions to regression-based analysis through SURVEYREG and SURVEYLOGISTIC. For longitudinal surveys and follow-up studies SURVEYPHREG is designed to incorporate aspects of the survey design for analysis of time-to-event outcomes based on the Cox proportional hazards model, allowing for time-varying explanatory variables	sample, proc step, proc calis, proc phreg, regression analysis, likelihood function, sample weight, covariance, proc reg, degrees of freedom, logistic distribution, hazard ratio, logistic regression, survival analysis, estimate, right censoring, sample size, censoring indicator, maximum likelihood estimation	source	17
324	Table Lookup Techniques: From the Basics to the Innovative	Arthur L. Carpenter	One of the more commonly needed operations within SAS programming is to determine the value of one variable based on the value of another. A series of techniques and tools have evolved over the years to make the matching of these values go faster, smoother, and easier. A majority of these techniques requir e operations such as sorting, searching, and comparing. As it turns out, these types of techniques are some of the more computationally intensive, and consequently an understanding of the operations involved and a careful selection of the specific techniq ue can often save the user a substantial amount of computing resources. Many of the more advanced techniques can require substantially fewer resources. It is incumbent on the user to have a broad understanding of the issues involved and a more detailed understanding of the solutions available	set statement, hash table, by group processing, merge statement, lookup, proc step, proc sql, data merging, proc format, key=, select, if-then-else, length statement, proc sort, index, dataset, key, conditional do loop	source	11
325	Tactical Marketing with SAS Visual Analytics--Aligning a Customer's Online Journey with In-Store Purchases	Patricia S. Aanderud, Johann Pasion	Marketers often face a cross-channel challenge in making sense of the behavior of web visitors who spend considerable time researching an item online, even putting the item in a wish list or checkout basket, but failing to follow up with an actual purchase online, instead opting to purchase the item in the store. This research shows the use of SAS Visual Analytics to address this challenge. This research uses a large data set of simulated web transactional data, combines it with common IDs to attach the data to in-store retail data, and studies it in SAS Visual Analytics. In this presentation, we go over tips and tricks for using SAS Visual Analytics on a non-distributed server. The loaded data set is analyzed step-by-step to show how to draw correlations in the web browsing behavior of customers and how to link the data to their subsequent in-store behavior. It shows how we can draw inferences between web visits and in-store visits by department. You'll change your marketing strategy as a result of the research	big data, proof of concept	source	10
326	Take a Bite Out of Crime with SAS Visual Scenario Designer	Yue Qi	The vast and increasing demands of fraud detection and description have promoted the broad application of statistics and machine learning in fields as diverse as banking, credit card application and usage, insurance claims, trader surveillance, health care claims, and government funding and allowance management. SAS Visual Scenario Designer enables you to derive interactive business rules, along with descriptive and predictive models, to detect and describe fraud and money laundering. This paper focuses on building interactive decision trees to classify fraud. Attention to optimizing the feature space (candidate predictors) prior to modeling is also covered. Because big data plays an increasingly vital role in fraud detection and description, SAS Visual Scenario Designer leverages the in-memory, parallel, and distributed computing abilities of a SAS in-memory server as a back end to support real-time performance on massive amounts of data	proc hpsplit, outlier, feature importance, data wrangling, claims fraud, stream processing, real-time processing, insurance claims, machine learning	source	10
327	Take Your Data Analysis and Reporting to the Next Level by Combining SAS Office Analytics, SAS Visual Analytics, and SAS Studio	David Bailey, Tim Beese, Casey Smith	SAS Office Analytics, SAS Visual Analytics, and SAS Studio provide excellent data analysis and report generation. When these products are combined, their deep interoperability allows you to take your analysis and reporting to the next level. Build interactive reports in SAS Visual Analytics Designer, and then view, customize, and comment on these reports in Microsoft Office and SAS Enterprise Guide. Create stored processes in SAS Enterprise Guide, and then run them in SAS Visual Analytics Designer, mobile tablets, or SAS Studio. Run your SAS Studio tasks in SAS Enterprise Guide and Microsoft Office using data provided by those applications. These interoperability examples and more will allow you to combine and maximize the strength of each SAS application	sas enterprise guide, sas studio, excel, dataset	source	18
328	Taking the Path More Travelled--SAS Visual Analytics and Path Analysis	Falko Schulz, Olaf Kratzsch	Understanding the behavior of your customers is key to improving and maintaining revenue streams. It is a critical requirement in the crafting of successful marketing campaigns. Using SAS Visual Analytics , you can analyze and explore user behavior, click paths , and other event-based scenarios. Flow visualizations help you to better understand hotspots, highlight common trends, and find insights in individual use r or aggregated paths. This paper explains the basic concepts of path analysis as well as provide s detailed background information about how to use flow visualizations within SAS Visual Analytics	customer experience, exploratory data analysis	source	14
329	Teach Them to Fish--How to Use Tasks in SAS Studio to Enable Co-Workers to Run Your Reports Themselves	Christie Corcoran, Amy Peters	How many times has this happened to you? You create a really helpful report and share it with others. It becomes popular and you find yourself running it over and over. Then they start asking, "But cannot you re-run it and just change ___?" (Fill in the blank with whatever "simple" request you can think of.) Don't you want to just put the report out as a web page with some basic parameters that users can choose themselves and run when they want? Consider writing your own task in SAS Studio! SAS Studio includes several predefined tasks, which are point-and-click user interfaces that guide the user through an analytical process. For example, tasks enable users to create a bar chart, run a correlation analysis, or rank data. When a user selects a task option, SAS code is generated and run on the SAS server. Because of the flexibility of the task framework, you can make a copy of a predefined task and modify it or create your own	sas studio, macro variable, sample	source	13
330	Tell Me What You Want: Conjoint Analysis Made Simple Using SAS	Delali Agbenyegah	The measurement of factors influencing consumer purchasing decisions is of interest to all manufacturers of goods, retailers selling these goods, and consumers buying these goods. In the past decade, conjoint analysis has become one of the commonly used statistical techniques for analyzing the decisions or "trade -offs" consumers make when they purchase products. Although recent years have seen increased use of conjoint analysis and conjoint software, there is limited work that has spelled out a systematic procedure on how to do a conjoint analysis or use conjoint software. The goals of this paper are as follow: 1 ) Review basic conjoint analysis concepts,2) Describe the mathematical and statistical framework on which conjoint analysis is built; 3) Introduce the TRANSREG and PHREG procedures, their syntaxes, and the output they generate using simplified real life da ta examples	conjoint analysis, proc step, logistic regression, proc phreg, anova, likelihood function, main effect	source	14
331	Ten Tips for Simulating Data with SAS	Rick Wicklin	Data simulation is a fundamental tool for statistical programmers. SAS software provides many techniques for simulating data from a variety of statistical models. However, not all techniques are equally efficient. An efficient simulation can run in seconds, whereas an inefficient simulation might require days to run. This paper presents 10 techniques that enable you to write efficient simulations in SAS. Examples include how to simulate data from a complex distribution and how to use simulated data to approximate the sampling distribution of a statistic	sample, residual, predictor variable, proc step, regression analysis, probability distribution, proc iml, standard deviation	source zip	15
332	Text Mining Kaiser Permanente Member Complaints with SAS Enterprise Miner	Amanda Pasch	Presentation	data mining, data cleaning, sas enterprise guide	source	5
333	The %ic_mixed Macro: A SAS Macro to Produce Sorted Information Criteria (AIC and BIC) List for PROC MIXED for Model Selection	Qinlei Huang	`PROC MIXED` is one of the most popular SAS procedures to perform longitudinal analysis or multilevel models in Epidemiology. Model selection is one of the fundamental questions in model building. One of the most popular and widely used strategies is model selection based on info rmation criteria, such as Akaike Information Criterion (AIC) and Sawa Bayesian Information Criterion (BIC). It considers both fit and complexity, and enables multiple models to be compared simultaneously. However, there is no existing SAS procedure to perform model selection automatically based on Information Criteria for `PROC MIXED`, given a set of covariates. This paper provides a SAS macro %IC_MIXED to select a final model with the smallest value of AIC/BIC	proc mixed, feature selection, covariance, proc step, macro definition, interaction effect, dataset, akaike information criterion, proc glm, residual, response variable, correlation, likelihood function, proc report, autocorrelation, bayesian information criterion, maximum likelihood estimation, do loop, fixed effects, between-subject variability, longitudinal data, unstructured covariance	source	16
334	The Advantages and Pitfalls of Implementing SAS in an Amazon Web Services Cloud Instance	Jeff Lehmann	The implementation of the best in breed advanced analytics software, SAS, on a cloud instance managed by the largest cloud service competitor, Amazon Web Services (AWS), is likely to create a compelling solution. This paper nonetheless will describe not just the positive aspects of such a combination but will delineate any potential issues and concerns	sas administrator, service provider	source	4
335	The Analytics Behind an NBA Name Change	Lauren Cook	Presentation	proc step, big data	source zip	3
336	The Best DATA Step Debugging Feature	Steven J. First	Many languages including the SAS `DATA STEP` have extensive debuggers that can be used to detect logic errors in programs. Another easy way to detect logic errors is to simply display messages and variable content at strategic places. The `PUTLOG` statement will be discussed and examples given that make this probably the easiest and most flexible way to get your `DATA STEP` logic correct	data step, input statement, cards	source	3
337	The bookBot Ultimatum!	David A. Dickey, John Vickery	The bookBot Identity: January 2013. With no memory of it from the past, students and faculty at NC State awake to find the Hunt Library just opened, and inside it, the mysterious and powerful bookBot. A true physical search engine, the bookBot, without thinking, relentlessly pursues, captures, and delivers to the patron any requested book (those things with paper pages-remember?) from the Hunt Library. The bookBot Supremacy: Some books were moved from the central campus library to the new Hunt Library. Did this decrease overall campus circulation or did the Hunt Library and its bookBot reign "supreme" in increasing circulation? The bookBot Ultimatum: To find out if the opening of the Hunt Library decreased or increased overall circulation. To address the bookBot Ultimatum, the Circulation Statistics Investigation (CSI) team uses the power of SAS analytics to model library circulation before and after the opening of the Hunt Library	binary variable, proc step, time series analysis, sample, correlation, proc arima, proc ttest, residual, proc reg, regression analysis, proc calis, macro variable, homoscedasticity, qq plot, seasonality, proc sql	source zip	19
338	The Business of Campaign Response Tracking	Pamela Dixon	Tracking responses is one of the most important aspects of the campaign life cycle for a marketing analyst, yet this is often a daunting task. This paper provides guidance for how to determine what is a response, how to define a response for your business, and how to collect response data. You can apply this guidance when using SAS Marketing Automation and more generally, when assessing the effectiveness of a marketing campaign	campaign response, sample, data validation rules, data standard	source	8
339	The Comparative Analysis of Predictive Models for Credit Limit Utilization Rate with SAS/STAT	Denys Osipenko, Jonathan Crook	Credit card usage modelling is a relatively innovative task of client predictive analytics compared to risk modelling such as credit scoring. The credit limit utilization rate is a problem with limited outcome values and highly dependent on customer behavior. Proportion prediction techniques are widely used for Loss Given Default estimation in credit risk modelling (Belotti and Crook, 2009; Arsova et al, 2011; Van Berkel and Siddiqi, 2012; Yao et al, 2014). This paper investigates some regression models for utilization rate with outcome limits applied and provides a comparative analysis of the predictive ac curacy of the methods. Regression models are performed in `SAS/STAT` using `PROC REG`, `PROC LOGISTIC`, `PROC NLMIXED`, `PROC GLIMMIX`, and SAS macros for model evaluation. The conclusion recommends credit limit utilization rate prediction techniques obtained from the empirical analysis	regression analysis, logistic regression, logistic distribution, proc reg, sample, credit card, likelihood function, data cleaning, lgd, beta distribution, credit risk, proc mixed, probability distribution, interest rate, proc step, proc genmod	source	19
340	The Essentials of SAS Dates and Times	Derek P. Morgan	The first thing you need to know is that SAS software stores dates and times as numbers. However, this is not the only thing that you need to know, and this presentation will give you a solid base for working with dates and times in SAS. It will also introduce you to functions and features that will enable you to manipulate your dates and times with surprising flexibility. This paper will also show you some of the possible pitfalls with dates (and times and datetimes) in your SAS code, and how to avoid them. We'll show you how the SAS System handles dates and times through examples, including the ISO 8601 formats and informats, how to use dates and times in TITLE and/or FOOTNOTE statements, and close with a brief discussion of `EXCEL` conversions	sysdate9, date variable, macro variable, excel, proc format, flat file, missing value, error message, proc step, user-defined format, character variable, input statement, numeric variable, time, time., macro function	source zip	18
341	The Great Dilemma of Row-Level Permissions for LASR Tables	Emre Saricicek, Dean Huff	Many industries are challenged with requirements to protect information and limit its access. In this paper, we will discuss various approaches for row-level access to LASR tables and demonstrate our implementation. Methods discussed in this paper include security joins in data queries, using star schema with security table as one dimension, permission conditions based on metadata stored user information, and user IDs being associated with data as a dedicated column. The paper then identifies shortcomings and strengths of various approaches as well as our iterations to satisfy business needs that led us to our row-level permissions implementation. In addition, the paper offers recommendations and other considerations to keep in mind while working on row-level permissions with LASR tables	star schema, bank transaction	source	8
342	The Impact of Hadoop Resiliency on SAS LASR Analytic Server	Robert Collum	The SAS LASR Analytic Server acts as a back-end, in-memory analytics engine for solutions such as SAS Visual Analytics and SAS Visual Statistics. It is designed to exist in a massively scalable, distributed environment, often alongside Hadoop. This paper guides you through the impacts of the architecture decisions shared by both software applications and what they specifically mean for SAS. You will also see positive actions you can take to rebound from unexpected outages and resume efficient operations	in-memory analytics, sas base	source	13
343	The Knight's Tour in Chess - Implementing a Heuristic Solution	John R. Gerlach	The Knight's Tour is a sequence of moves on a chess board such that a knight visits each square only once. Using a heuristic method, it is possible to find a complete path, beginning from any arbitrary square on the board and landing on the remaining squares only once. However, the implementation poses challenging programming problems. For example, it is necessary to discern viable knight moves, which changes throughout the tour. Even worse, the heuristic approach does not guarantee a solution. This paper explains a SAS solution that finds a knight's tour beginning from every initial square on a chess board - Well, almost	macro variable, macro loop	source	8
344	The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner	Narmada Deve Panneerselvam	Random Forest (RF) is a trademark term for an ensemble approach of Decision Trees. RF was introduced by Leo Breiman in 2001.This paper demonstrates this simple yet powerful cl assification algorithm by building an income -level prediction system. Data extracted from the 1994 Census Bureau database was used for this study. The data set comprises information about 14 key attributes for 45,222 individuals. Using SAS Enterprise Mine r 13.1, models such as random forest, decision tree, neural network, gradient boosting, and logistic regression were built to classify the income level( >50K or <50k) of the population. The results showed that, the random forest model was the best model f or this data, based on the misclassification rate criteria. The RF model predicts the income -level group of the individuals with an accuracy of 86.23%, with the predictors capturing specific characteristic patterns	proc hpforest, data mining, bootstrap, sample, proc step, regression analysis	source	10
345	The REPORT Procedure: A Primer for the Compute Block	Jane Eslinger	It is well-known in the world of SAS programming that the `REPORT` procedure is one of the best procedures for creating dynamic reports. However, you might not realize that the compute block is where all of the action takes place! Its flexibility enables you to customize your output. This paper is a primer for using a compute block. With a compute block, you can easily change values in your output with the proper assignment statement and add text with the LINE statement. With the CALL DEFINE statement, you can adjust style attributes such as color and formatting. Through examples, you learn how to apply these techniques for use with any style of output. Understanding how to use the compute-block functionality empowers you to move from creating a simple report to creating one that is more complex and informative, yet still easy to use	proc report, proc step, automatic variable, character variable, numeric variable, response variable, missing value	source	23
346	The SAS Scalable Performance Data Engine: Moving Your Data to Hadoop without Giving Up the SAS Features You Depend On	Lisa Brown	If you are one of the many customers who want to move your SAS data to Hadoop, one decision you will encounter is what data storage format to use. There are many choices, and all have their pros and cons. One factor to consider is how you currently store your data. If you currently use the Base SAS engine or the SAS Scalable Performance Data (SPD) Engine, then using the SPD Engine with Hadoop will enable you to continue accessing your data with as little change to your existing SAS programs as possible. This paper discusses the enhancements, usage, and benefits of the SPD Engine with Hadoop	sas base, libname engine, proc step, missing value, options statement, proc sql	source	10
347	The SAS Transformation Project--Deploying SAS Customer Intelligence for a Single View of the Customer	Ronak Shah, Minza Zahid	Building a holistic view of the customer is becoming the norm across industries. The financial services industry and retail firms have been at the forefront of striving for this goal. Firm ABC is a large insurance firm based in the United States. It uses multiple campaign management platforms across different lines of business. Marketing campaigns are deployed in isolation. Similarly, responses are tracked and attributed in silos. This prevents the firm from obtaining a holistic view of its customers across products and lines of business and leads to gaps and inefficiencies in data management, campaign management, reporting, and analytics	data cleaning, data governance	source	7
348	The SEM Approach to Longitudinal Data Analysis Using the CALIS Procedure	Xinming An, Yiu-Fai Yung	Researchers often use longitudinal data analysis to study the development of behaviors or traits. For example, they might study how an elderly person's cognitive functioning changes over time or how a therapeutic intervention affects a certain behavior over a period of time. This paper introduces the structural equation modeling (SEM) approach to analyzing longitudinal data. It describes various types of latent curve models and demonstrates how you can use the `CALIS` procedure in `SAS/STAT` software to fit these models. Specifically, the paper covers basic latent curve models, such as unconditional and conditional models, as well as more complex models that involve multivariate responses and latent factors. All illustrations use real data that were collected in a study that looked at maternal stress and the relationship between mothers and their preterm infants. This paper emphasizes the practical aspects of longitudinal data analysis	random intercept, sbc, proc step, covariance, random slope, treatment effect, correlation	source	18
349	The Spatio-Temporal Impact of Urgent Care Centers on Physician and ER Use	Laurel Trantham	The unsustainable trend in healthcare costs has led to efforts to shift some healthcare services to appropriate lower cost sites of care. In North Carolina, the expansion of urgent care clinics introduces the possibility that non-emergent and non-life threatening conditions can be treated at a less intensive setting. Blue Cross and Blue Shield of North Carolina (BCBSNC) conducted a longitudinal study of density of urgent care clinics, primary care providers, and emergency departments, and the differences in how patients access care near those locations. This paper focuses on several analytic techniques that were considered for the analysis. The model needed to account for the complex relationship between the changes in the population (including health conditions and health insurance benefits) over time and the changes in the types of services offered by healthcare providers proximal to them. Results for the chosen methodology are discussed	response variable, proc step, regression analysis, logistic regression, proc genmod, logistic distribution	source	6
350	The Use Of SAS Maps For University Retention And Recruiting	Allison Lempola, Thomas Brandenburger	Universities often have student data that is difficult to represent, which includes information about the student's home location. Often, student data is represented in tables, and patterns are easily overlooked. This study aimed to represent recruiting and retention data at a county level using SAS mapping software for a public, land-grant university. Three years of student data from the student records database were used to visually represent enrollment, retention, and other predictors of student success. SAS Enterprise Guide was used along with the `GMAP` procedure to make user-friendly maps. Displaying data using maps on a county level revealed patterns in enrollment, retention, and other factors of interest that might have otherwise been overlooked, which might be beneficial for recruiting purposes	proc gmap, proc format, proc step, proc sql	source	7
351	Thoughts on SAS Visual Analytics Architecture for Multiple Customer Groups	Jan Bigalke	SAS Visual Analytics is a product to easily allow the interactive analysis of data. The product offers capabilities to analyze data with a visual approach. This paper discusses architecture options to configure a visual analytics installation that serves multiple customers in parallel. The overall objective is to create an environment that scales with the volume of data and also with the number of customer groups. This paper will explain several concepts to serve multiple customers and explain the pro and cons of each approach	sas visual analytics, memory usage	source	6
352	Three S's in SAS Visual Analytics: Stored Process, Star Schema, and Security	Arun Sugumar, Vimal Raj	SAS Visual Analytics is very responsive in analyzing historical data, and it takes advantage of in-memory data. Data query, exploration, and reports form the basis of the tool, which also has other forward-looking techniques such as star schemas and stored processes. A security model is established by defining the permissions through a web-based application that is stored in a database table. That table is brought to the SAS Visual Analytics environment as a LASR table. Typically, security is established based on the departmental access, geographic region, or other business-defined groups. This permission table is joined with the underlying base table. Security is defined by a data filter expression through a conditional grant using SAS metadata identities. The in-memory LASR star schema is very similar to a typical star schema. A single fact table that is surrounded by dimension tables is used to create the star schema	star schema, fact table, dimension table	source	10
353	Time Series Modeling and Forecasting--An Application to Stress-Test Banks	Ania Supady	This paper provides with basic understanding of how large US bank holding companies (hereafter, BHC) can perform stress testing. It explains how banks in general can perform time series modeling of balance sheet to establish bank's position during simulated stress. It gives overview of basic process behind model building and validation for Comprehensive Capital Analysis and Review ( hereafter, CCAR) purposes, which includes, but is not limited to, back testing, sensitivity analysis, scenario analysis, and model assumption testing. This paper examines the procedures that happen behind the scenes of model forecasting process as well as explores statistics which play crucial role in assessing model performance and forecasting	regression analysis, time series analysis, stress testing, confidence interval, response variable, residual, scenario analysis, sensitivity analysis, sample, predictor variable, correlation, proc arima, capital adequacy, unit root, data cleaning, maximum likelihood estimation, training set	source	13
354	Time to Harvest: Operationalizing SAS Analytics on the SAP HANA Platform	Morgen Christoph	The ultimate value of farming analytics efforts in organizations with a maximum harvest is only achieved if analytics can also be operationalized at the level of core business applications. Mapped to the use of SAS Analytics, how can the fruits of SAS be shared with Enterprise Business Applications by SAP? Learn how your SAS environment - including the latest of SAS In-Memory Analytics - can be integrated with SAP applications based on SAP's In-Memory Platform SAP HANA. We'll explore how a SAS Predictive Modeling environment can be embedded inside SAP HANA and how native SAP HANA data management capabilities like SAP HANA views, smart data access and more can be leveraged by SAS applications and thus contribute to an end-to-end in-memory data management and analytics platform. Come and see how you can extend the reach of your SAS Analytics efforts with the SAP HANA integration	proc sql, data mining, sas model manager, score code, sql pass-through, proc step, data governance, stored procedure, data wrangling	source	11
355	Tips and Techniques for Efficiently Updating and Loading Data into SAS Visual Analytics	Kerri L. Rivers, Christopher Redpath	So you have big data and need to know how to quickly and efficiently keep your data up-to-date and available in SAS Visual Analytics? One of the challenges that customers often face is how to regularly update data tables in the SAS LASR Analytic Server, the in-memory analytical platform for SAS Visual Analytics. Is appending data always the right answer? What are some of the key things to consider when automating a data update and load process? Based on proven best practices and existing customer implementations, this paper provides you with answers to those questions and more, enabling you to optimize your update and data load processes. This ensures that your organization develops an effective and robust data refresh strategy	proc step, dataset, star schema, fact table, dimension table, character variable, analytics platform, proc sql	source	13
356	Tips and Tricks for SAS Program Automation	Adam Hood	Presentation	macro variable, macro definition, macro put, proc step	source	4
357	Tips for Managing SAS Work Libraries	Thomas E. Billings, Avinash Kalwani	The Work library is at the core of most SAS programs, but programmers tend to ignore it unless something breaks. This paper first discusses the USER= system option for saving the Work files in a directory. Then, we cover a similar macro-controlled method for saving the files in your Work library, and the interaction of this method with OPTIONS NOREPLACE and the syntax check options. A number of SAS system options that help you to manage Work libraries are discussed: WORK=, WORKINIT, WORKTERM; these options might be restricted by SAS Administrators. Additional considerations in managing Work libraries are discussed: handling large files, file compression, programming style, and macro-controlled deletion of redundant files in SAS Enterprise Guide	work library, options statement, sas enterprise guide, proc step, macro variable, permanent library	source zip	9
358	Tips for Publishing in Health Care Journals with the Medical Expenditure Panel Survey (MEPS) Data Using SAS	Seungyoung Hwang	Recent decades have witnessed a large increase in academic interest in health services research. The Medical Expenditure Panel Survey (MEPS) data released from the Agency for Healthcare Research and Quality (AHRQ) have been important tools for healthcare services research and policy i n an era of health care reform. In this paper, the analysis proceeds in two phases. First, we outline eight recently published articles on health services use and expenditures associated with specific medical conditions using the MEPS data. Second, we conduct and extend their statistical analyses, considering depression with heart disease as the key variable of interest. Examples of using the user-friendly survey sampling procedures are drawn from the 2012 MEPS data that were the latest available datasets at the time of study	sample, proc step, proc surveyfreq, proc genmod	source	14
359	To %Bquote or Not to %Bquote? That Is the Question (Which Drives SAS Macro Programmers around the Bend)	Andrew Howell	Presentation	macro function, macro variable, macro trigger, if-then-else, macro quoting function, %if-%then-%else, symbol table	source	17
360	To Believe or Not To Believe? The Truth of Data Analytics Results	Mike Hardin	Presentation	correlation, regression analysis, data mining, hypothesis testing, bayesian statistics, proc cancorr, data warehouse	source	71
361	Transpose Data Sets by Merge	Xia Ke Shan, Matthew J. Kastin, Arthur S. Tabachneck	Using `PROC TRANSPOSE` to make wide files wider requires running separate `PROC TRANSPOSE` steps for each variable that you want transposed, as well as a `DATA STEP` using a `MERGE` statement to combine all of the transposed files. Additionally, if you want the variables in a specific order, an extra `DATA STEP` will be needed to rearrange the variable ordering	proc sql, proc step	source zip	4
362	Tweet-O-Matic: An Automated Approach to Batch Processing of Tweets	Isabel Litton, Rebecca Ottesen	Currently, there are several methods to read JSON formatted files into SAS which depend on the version of SAS and which products are licensed. These methods include user-defined macros, visual analytics, `PROC GROOVY` and more. The user defined macro %GrabTweet, in particular, provides a simple way to directly read JSON formatted tweets into Base SAS 9.3. The main limitation of %GrabTweet is that it requires the user to repeatedly run the macro in order to download large amounts of data over time. Manually downloading tweets while adhering to the Twitter rate limits may cause missing observations and is time consuming overall. Imagine having to sit by your computer the entire day to continuously grab data every 15 minutes, just to download a complete data set of tweets for a popular event. Fortunately, the %GrabTweet macro can be modified to automate of the retrieval of Twitter data based on the rate that the tweets are coming in	batch processing, proc step, proc http, macro variable, call symput, sas base, &variable, txt, conditional do loop, macro loop, proc sql, %if-%then-%else	source	13
363	Twelve Ways to Better Graphs	LeRoy Bessler	If you are looking for ways to make your graphs more communication-effective, this tutorial can help. It covers both the new `ODS` Graphics SG (Statistical Graphics) procedures, and the traditional `SAS/GRAPH` software G procedures. The focus is on management reporting and presentation graphs, but the principles are relevant for statistical graphs as well. Important features unique to SAS 9.4 are included, but most of the designs and construction methods apply to earlier versions as well. The principles of good graphic design are actually independent of your choice of software	bar chart, line chart, sample, time series analysis	source	20
364	Unconventional Data-Driven Methodologies Forecast Performance in Unconventional Oil and Gas Reservoirs	Keith R. Holdaway, Louis Fabbi, Dan Lozie	How do historical production data relate a story about subsurface oil and gas reservoirs? Business and domain experts must perform accurate analysis of reservoir behavior using only rate and pressure data as a function of time. This paper introduces innovative data-driven methodologies to forecast oil and gas production in unconventional reservoirs that, owing to the nature of the tightness of the rocks, render the empirical functions less effective and accurate. You learn how implementations of the SAS `MODEL` procedure provide functional algorithms that generate data-driven type curves on historical production data. Reservoir engineers can now gain more insight to the future performance of the wells across their assets. SAS enables a more robust forecast of the hydrocarbons in both an ad hoc individual well interaction and in an automated batch mode across the entire portfolio of wells	confidence interval, bootstrap, cluster analysis, data mining, neural network, correlation, proc model, regression analysis, prior probability, sample, proc princomp	source	20
365	Understanding Characteristics of Insider Threats by Using Feature Extraction	Ila Gokarn, Clifton Phua	This paper explores feature extraction from unstructured text variables using Term Frequency - Inverse Document Frequency (TF-IDF) weighting algorithms coded in Base SAS. Datasets with unstructured text variables can often hold a lot of potential to enable better predictive analysis and document clustering. Each of these unstructured text variables can be utilized as inputs to build an enriched dataset-specific inverted index and the most significant terms from this index can be used as single word queries to weight the importance of the term to each document from th e corpus. This paper also explores the usage of hash objects to build the inverted indices from the unstructured text variables. We find that hash objects provide a considerable increase in algorithm efficiency and our experiments show that a novel weighting algorithm proposed by Paik (2013) best enables meaningful feature extraction	proc freq, data mining, normalization, proc hpsplit, hash table, character variable, feature extraction, conditional do loop, feature selection, sas base, credit card, macro variable, proc varclus, roc curve	source	14
366	Understanding Patient Populations in New Hampshire using SAS Visual Analytics	Danna Hourani	As part of its Accountable Care Project (ACP), the Institute for Health Policy and Practice at the University of New Hampshire has developed a set of analytic reports to provide healthcare systems undergoing transformation a capacity to compare measures of quality, utilization, and cost across systems and regions. The purpose of these reports is to provide data and analysis on which the ACP learning collaborative can share knowledge and develop action plans that can be adopted by healthcare innovators in New Hampshire. This paper showcases the claims-based reports, powered by SAS Visual Analytics and driven by the New Hampshire Comprehensive Health Care Information System (CHIS), which includes commercial, Medicaid, and Medicare populations. With the power of SAS Visual Analytics, thousands of pages of PDF files were distilled down to a manageable, dynamic, web-based portal that allows users to target information most appealing to them. This streamlined approach reduces barriers to obtaining information, offers that information in a digestible medium, and creates a better user experience		source	5
367	Understanding Patterns in the Utilization and Costs of Elbow Reconstruction Surgeries: A Healthcare Procedure that is Common among Baseball Pitchers	Jeff Dang	Elbow reconstruction surgeries are commonly utilized by athletes in sports such as baseball and softball. Research suggests that the rate of elbow reconstruction surgeries among professional baseball pitchers continues to rise. Given the trend found among professional pitchers, the current study looks to review patterns of elbow reconstruction surgery among the privately insured population. The study examined trends (for example, cost, age, geography, and utilization) in elbow reconstruction surgeries among privately insured patients using analytic tools such as SAS Enterprise Guide and SAS Visual Analytics based on the Medical/Surgical claims data from the FAIR Health National Private Insurance Claims (FH NPIC) database. The findings of the study suggested that there are discernable patterns in the prevalence of elbow reconstruction surgeries over time and across specific geographic regions	sample, data cleaning	source zip	8
368	Unraveling the Knot of Ampersands	Joe Matise	We've all heard it before: "If two ampersands don't work, add a third." But how many of us really know how ampersands work behind the scenes? We show the function of multiple ampersands by going through examples of the common two - and three-ampersand scenarios, and expand to show four, five, six, and even seven ampersands, and explain when they might be (rarely) useful	macro variable, &variable, symbol table, global symbol table, macro put, sas programmer, macro definition, macro quoting function	source	8
369	Unstructured Data Mining to Improve Customer Experience in Interactive Voice Response Systems	Dmitriy Khots	Interactive Voice Response (IVR) systems are likely one of the best and worst gifts to the world of communication, depending on who you ask. Businesses love IVR systems because they take out hundreds of millions of dollars of call center costs in automation of routine tasks, while consumers hate IVRs because they want to talk to an agent! It is a delicate balancing act to manage an IVR system that saves money for the business, yet is smart enough to minimize consumer abrasion by knowing who they are, why they are calling, and providing an easy automated solution or a quick route to an agent. There are many aspects to designing such IVR systems, including engineering, application development, omni - channel integration, user interface design, and data analytics. For larger call volume businesses, IVRs generate terabytes of data per year, with hundreds of millions of rows per day that track all system and customer- facing events	score code, proc sql, data mining, customer experience, customer satisfaction, sql pass-through, unstructured data, web service, proc step	source	11
370	Update Microsoft Excel While Maintaining Your Formats	Brian Wrobel	There have been many SAS Global Forum papers written about getting your data into SAS from Microsoft `EXCEL` and getting your data back out to `EXCEL` after using SAS to manipulate it. But sometimes you have to update `EXCEL` files with special formatting and formulas that would be too hard to replicate with Dynamic Data Exchange (DDE) or that change too often to make DDE worthwhile. But we can still use the output prowess of SAS and a sprinkling of Visual Basic for Applications (VBA) to maintain the existing formatting and formulas in your `EXCEL` file! This paper focuses on the possibilities you have in updating `EXCEL` files by reading in the data, using SAS to modify it as needed, and then using DDE and a simple `EXCEL` VBA macro to output back to `EXCEL` and use a formula while maintaining the existing formatting that is present in your source `EXCEL` file	excel, proc export	source zip	4
371	Using and Understanding the LSMEANS and LSMESTIMATE Statements	David J. Pasta	The concept of least squares means, or population marginal means, seems to confuse a lot of people. We explore least squares means as implemented by the LSMEANS statement in SAS, beginning with the basics. Particular emphasis is paid to the effect of alternative parameterizations (for example, whether binary variables are in the CLASS statement) and the effect of the OBSMARGINS option. We use examples to show how to mimic LSMEANS using ESTIMATE statements and the advantages of the relatively new LSMESTIMATE statement. The basics of estimability are discussed, including how to get around the dreaded "non-estimable" messages. Emphasis is put on using the STORE statement and `PROC PLM` to test hypotheses without having to redo all the model calculations. This material is appropriate for all levels of SAS experience, but some familiarity with linear models is assumed	least squares mean, binary variable, proc plm, proc step, proc genmod, p-value, proc calis, sample, interval variable, estimate, main effect	source	20
372	Using Boolean Rule Extraction for Taxonomic Text Categorization for Big Data	Zheng Zhao, Russell Albright, James A. Cox, Ning Jin	Categorization hierarchies are ubiquitous in big data. Examples include MEDLINE's Medical Subject Head- ings (MeSH) taxonomy, United Nations Standard Products and Services Code (UNSPSC) product codes, and the Medical Dictionary for Regulatory Activities (MedDRA) hierarchy for adverse reaction coding. A key issue is that in most taxonomies the probability of any particular example being in a category is very small at lower levels of the hierarchy. Blindly applying a standard categorization model is likely to perform poorly if this fact is not taken into consideration. This paper introduces a novel technique for text categorization, called Boolean rule extraction, which ena- bles you to effectively address this situation. In addition, models that are generated by this introduced rule- based technique can be easily interpreted and modified by a human expert, enabling better human-machine interaction		source	12
373	Using PROC SURVEYREG and PROC SURVEYLOGISTIC to Assess Potential Bias	Lucy D'Agostino McGowan, Alice Toll	The Behavioral Risk Factor Surveillance System (BRFSS) collects data on health practices and risk behaviors via telephone survey. This study focuses on the question, "On average, how many hours of sleep do you get in a 24-hour period?" Recall bias is a potential concern in interviews and questionnaires, such as BRFSS. The 2013 BRFSS data is used to illustrate the proper methods for implementing `PROC SURVEYREG` and `PROC SURVEYLOGISTIC`, using the complex weighting scheme that BRFSS provides	proc step, proc surveylogistic, proc surveyreg, imputation, sample, regression analysis, logistic regression, proc mianalyze, logistic distribution, proc reg, missing value, nominal variable, multiple imputation, proc calis	source	8
374	Using SAS Enterprise Guide, SAS Enterprise Miner, and SAS Marketing Automation to Make a Collection Campaign Smarter	Darwin A. Amezquita, Paulo Fuentes, Andres F. Gonzalez	Companies are increasingly relying on analytics as the right solution to their problems. In order to use analytics and create value for the business, companies first need to store, transform, and structure the data to make it ava ilable and functional. This paper shows a successful business case where the extraction and transformation of the data combined with analytical solutions were developed to automate and optimize the management of the collections cycle for a TELCO company ( DIRECTV Colombia). SAS Data Integration Studio is used to extract, process, and store information from a diverse set of sources. SAS Information Map is used to integrate and structure the created databases. SAS Enterprise Guide and SAS Enterprise Miner are used to analyze the data, find patterns, create profiles of clients, and develop churn predictive models. SAS Customer Intelligence Studio is the platform on which the collection campaigns are created, tested, and executed	data mining, sas enterprise guide	source	9
375	Using SAS Enterprise Miner to Predict Breast Cancer at an Early Stage	Gibson Ikoro, Beatriz de la Iglesia	Breast cancer is the leading cause of cancer -related deaths amo ng women worldwide, and its early detection can reduce mortality rate. Using a data set containing information about breast screening, we constructed a model that can provide early indication of a patient's tendency to develop breast cancer. This data set has information about breast screening from patients who were believed to be at risk of developing breast cancer. The most important aspect of this analysis is that we excluded all patients with symptoms commonly associated with breast cancer, while keepin g patients with symptoms that are less likely or unknown to be associated with breast cancer as input predictors. The hope was that a model could be developed that would identify women at high risk of developing breast cancer. This group could then be subj ected to more intense screening with a view to earlier detection of the cancer and thus improved outcomes	missing value, data cleaning, response variable, imputation, regression analysis, data mining, interval variable, multiple imputation, roc curve	source	9
376	Using SAS Macros to Flag Claims Based on Medical Codes	Andy Karnopp	Many epidemiological studies use medical claims to identify and describe a population . But finding out who was diagnosed, and who received treatment, isn't always simple. Each claim can have dozens of medical codes, with different types of codes for procedures, drugs, and diagnoses . Even a basic definition of treatment could require a search for any one of 100 different codes. A SAS macro may come to mind, but generalizing the macro to work with different codes and types will allow it to be reused in a variety of different scenarios. We'll look at a number of examples, starting with a single code type and variable. Then we'll consider multiple code variables, multiple code types, and multiple flag variables. We'll show how these macros can be combined and tailored to different data with minimal rework. Macro flexibility and reusability are also discussed, along with ways to keep our list of medical codes separate from our program	proc sql, proc step, macro variable, flag variable, if-then-else, insurance claims, macro definition	source zip	15
377	Using SAS Mapping Functionality to Measure and Present the Veracity of Location Data	Richard Self	Crowd sourcing of data is growing rapidly, based on smart devices equipped with assisted GPS location tagging of photos and mapping other aspects of the users' lives and activities. A fundamental assumption is made when such data is used that the reported locations are accurate within the usual GPS limitations of approximately 10m. However, as a result of a wide range of technical issues, it turns out that the accuracy of the reported locations is highly variable and cannot be relied on. Some locations are accurate but many are highly inaccurate and this can affect many of the decisions that are being made from the data. As an example of the use of location data, some shops are now tracking their customers in order to offer suitable messages and adverts when they detect that the customer is close to the shop in ord er to entice them into the shop	excel, proc step	source	7
378	Using SAS Text Analytics to Examine Labor and Delivery Sentiments on the Internet	Michael Wallis	In today's society, where seemingly unlimited information is just a mouse click away, many turn to social media, forums, and medical websites to research and understand how mothers feel about the birthing process. Mining the data in these resources helps provide an understanding of what mothers value and how they feel. This paper shows the use of SAS Text Analytics to gather, explore, and analyze reports from mothers to determine their sentiment about labor and delivery topics. Results of this analysis could aid in the design and development of a labor and delivery survey and be used to understand what characteristics of the birthing process yield the highest levels of importance. These resources can then be used by labor and delivery professionals to engage with mothers regarding their labor and delivery preferences	proc freq, proc step, order by	source	14
379	Using SAS to Combine Regression and Time Series Analysis on U.S. Financial Data to Predict the Economic Downturn	Avinash Kalwani, Nishant Vyas	During the financial crisis of 2007-2009, the U.S. labor market lost 8.4 million jobs, causing the unemployment rate to increase from 5% to 9.5%. One of the indicators for economic recession is negative gross domestic product (GDP) for two consecutive quarters. This paper combines quantitative and qualitative techniques to predict the economic downturn by forecasting recession probabilities. Data was collected from the Board of Governors of the Federal Reserve System and the Federal Reserve Bank of St. Louis, containing 29 variables and quarterly observations from 1976-Q1 to 2013-Q3	logistic regression, regression analysis, logistic distribution, backward selection, proc esm, data mining, response variable, time series analysis, data cleaning	source	6
380	Using SAS to Create Episodes-of-Hospitalization for Health Services Research	Meriç Osman, Jacqueline Quail, Nianping Hu, Nedeene Hudema	An essential part of health services research is de scribing the use and sequencing of a variety of hea lth services. One of the most frequently examined healt h services is hospitalization. A common problem in describing hospitalizations is that a patient might have multiple hospitalizations to treat the same h ealth issue. Specifically, a hospitalized patient might b e (1) sent to and returned from another facility in a single day for testing or a procedure, (2) transferred fro m one hospital to another, and/or (3) discharged ho me and re-admitted within 24 hours. In all cases, thes e hospitalizations are likely treating the same underlying health issue and should be considered as a single hospitalization episode	macro definition, macro loop	source	8
381	Using SAS to Deliver Web Content Personalization Using Cloud-Based Clickstream Data and On-premises Customer Data	Mahesh Subramanian, Suneel Grover	Real-time web content personalization is growing up, and the rise of marketing solutions that enable marketers to finely personalize web content for visitors based on array of data attributes is maturing. This paper discusses a phased approach in which enterprises capture clickstream data and bring that data in- house to be merged with other sources of customer data. Their analytics team can use this merged data to build sophisticated predictive models that can be deployed into real-time web-personalization applications. In the final phase, enterprises continuously improve their predictive models in an iterative process	predictive analytics, data mart, proc hpsplit, normalization, a/b testing, data validation rules, customer experience, interaction effect, logistic regression, score code, logistic distribution, sas base, sas enterprise guide, raw data, response variable, randomization, regression analysis, sample, main effect, outlier, data mining	source	24
382	Using SAS to Increase Profitability through Cluster Analysis and Simplicity: Follow the Money	Sherrie Rodriguez	Presentation	cluster analysis, response variable, likelihood function, logistic distribution, regression analysis, data cleaning	source	9
383	Using SAS to Manage SAS Users on a UNIX File System	Venkateswarlu Toluchuri	SAS platform administrators always feel the pinch of not having information on how much storage space is occupied by each user on one specific file system or in the entire environment. Sometimes the platform administrator does not have an access to all users' folders. So they have to plan for the worst. There are multiple approaches to tackle this problem. One of the better methods is to initiate an alert mechanism to notify a user when they are in the top 10 file system users on the system	data step, sas administrator	source	6
384	Using SAS/OR to Optimize the Layout of Wind Farm Turbines	Sherry Xu, Steven Gardner, Joshua Griffin, Baris Kacar, Jinxin Yi	A Chinese wind energy company designs several hundred wind farms each year. An important step in its design process is micrositing, in which it creates a layout of turbines for a wind farm. The amount of energy that a wind farm generates is affected by geographical factors (such as elevation of the farm), wind speed, and wind direction. The types of turbines and their positions relative to each other also play a critical role in energy production. Currently the company is using an open-source software package to help with its micrositing. As the size of wind farms increases and the pace of their construction speeds up, the open-source software is no longer able to support the design requirements. The company wants to work with a commercial software vendor that can help resolve scalability and performance issues	proc optlso, proc step, sas base, weibull distribution, proc fcmp	source	13
385	Using SAS/STAT Software to Implement a Multivariate Aadaptive Outlier Detection Approach to Distinguish Outliers from Extreme Values	Paulo Macedo	Hawkins (1980) defines an outlier as "an observation that deviates so much from other observations as to arouse the suspicion that it was generated by a different mechanism". To identify data outliers, a classic multivariate outlier detection approach implements the Robust Mahalanobis Distance Method by splitting the distribution of distance values in two subsets (within-the-norm and out-of-the-norm), with the threshold value usually set to the 97.5% Quantile of the Chi-Square distribution with p (number of variables) degrees of freedom and items whose distance values are beyond it are labeled out-of-the-norm. This threshold value is an arbitrary number, however, and it may flag as out-of-the-norm a number of items that are actually extreme values of the baseline distribution rather than outliers. Therefore, it is desirable to identify an additional threshold, a cutoff point that divides the set of out -of-norm points in two subsets - extreme values and outliers	outlier, sample, mahalanobis distance, sample size, covariance, numeric functions, normal distribution, proc robustreg, proc step, robust estimator, type i error, standard deviation, chi-square distribution, proc sort	source	12
386	Using Text from Repair Tickets of a Truck Manufacturing Company to Predict Factors that Contribute to Truck Downtime	Ayush Priyadarshi, Goutam Chakraborty	In this era of big data, the use of text analytics to discover insights is rapidly gaining popularity in businesses. On average, more than 80 percent of the data in enterprises may be unstructured. Text analytics can help discover key insights and extrac t useful topics and terms from the unstructured dat a. The objective of this paper is to build a model using textual data that predicts the factors that contribute to downtime of a truck. This research analyzes the dat a of over 200,000 repair tickets of a leading truck manufacturing company. After the terms were grouped into ten key topics using text topic node of SAS Text Miner, a regression model was built using thes e topics to predict truck downtime, the target vari able. Data was split into training and validation for dev eloping the predictive models. Knowledge of the fac tors contributing to downtime and their associations hel ped the organization to streamline their repair pro cess and improve customer satisfaction	regression analysis, data mining, parsing, data cleaning	source	9
387	Using the GLIMMIX and GENMOD Procedures to Analyze Longitudinal Data from a Department of Veterans Affairs Multisite Randomized Controlled Trial	Abbas S. Tavakoli, Marlene Al-Barwani, Sue Levkoff, Selina McKinney, Nikki R. Wooten	Many SAS procedures can be used to analyze longitudinal data. This study employed a multisite randomized controlled trial design to demonstrate the effectiveness of two SAS procedures, GLIMMIX and GENMOD, to analyze longitudinal data from five Department of Veteran Affairs Medical Centers (VAMCs). Older male veterans (n = 1222) seen in VAMC primary care clinics were randomly assigned to two behavioral health models, integrated (n = 605) and enhanced referral (n = 617). Data were collected at baseline, and 3, 6, and 12 month follow-up. A mixed-effects repeated measures model was used to examine the dependent variable, problem drinking, which was defined as count and dichotomous from baseline to 12 month follow-up. Sociodemographics and depressive symptoms were included as covariates. First, bivariate analyses included general linear model and chi-square tests to examine covariates by group and group by problem drinking outcomes	longitudinal data, proc genmod, frequency analysis, proc glimmix, proc glm, interaction effect, by group processing, group by, proc mixed, proc freq, proc step	source	11
388	Using the OPTMODEL Procedure in SAS/OR to Solve Complex Problems	Robert C. Pratt	Mathematical optimization is a powerful paradigm for modeling and solving business problems that involve interrelated decisions about resource allocation, pricing, routing, scheduling, and similar issues. The `OPTMODEL` procedure in `SAS/OR` software provides unified access to a wide range of optimization solvers and supports both standard and customized optimization algorithms. This paper illustrates `PROC OPTMODEL` 's power and versatility in building and solving optimization models and describes the significant improvements that result from `PROC OPTMODEL` 's many new features. Highlights include the recently added support for the network solver, the constraint programming solver, and the COFOR statement, which allows parallel execution of independent solver calls. Best practices for solving complex problems that require access to more than one solver are also demonstrated	odds ratio, proc step	source zip	14
389	Using the PHREG Procedure to Analyze Competing-Risks Data	Ying C. So	Competing risks arise in studies in which individuals are subject to a number of potential failure events and the occurrence of one event might impede the occurrence of other events. For example, after a bone marrow transplant, a patient might experience a relapse or might die while in remission. Y ou can use one of the standard methods of survival analysis, such as the log-rank test or Cox regression, to analyze competing-risks data, whereas other methods, such as the product-limit estimator, might yield biased results. An increasingly common practice of assessing the probability of a failure in competing-risks analysis is to estimate the cumulative incidence function, which is the probability subdistribution function of failure from a specific cause. This paper discusses two commonly used regression approaches for evaluating the relationship of the covariates to the cause-specific failure in competing-risks data	cause-specific hazard, regression analysis, hazard function, proc phreg, hazard ratio	source	9
390	Using the SAS Output Delivery System (ODS) and the TEMPLATE Procedure to Replace Dynamic Data Exchange (DDE)	Peter Timusk	Many papers have been written over the years that describe how to use Dynamic Data Exchange (DDE) to pass data from SAS to `EXCEL`. This presentation aims to show you how to do the same exchange with the SAS Output Delivery System (`ODS`) and the `TEMPLATE` Procedure	excel, xls, proc template, proc step, output delivery system, ods excel	source zip	9
391	Using the SAS-PIRT Macro for Estimating the Parameters of Polytomous Items	Sung-Hyuck Lee	Polytomous items have been widely used in educational and psychological settings, with increasing demand for statistical programs that estimate the parameter s of polytomous items. Samejima (1969) proposed the graded response model (GRM) in which category characteristic curves are characterized by the difference of the two adjacent boundary characteristic curves. In this paper, it is demonstrated how the GRM parameter estimation program SAS -PIRT (SAS macro written in SAS `IML`) performs in recovering the parameters of polytomous items using simulated data	correlation, rmse, normal distribution, mse, likelihood function, maximum likelihood estimation	source	9
392	Validation and Monitoring of PD Models for Low Default Portfolios Using PROC MCMC	Machiel Kruger	A bank that wants to use the Internal Ratings Based (IRB) methods to calculate minimum Basel capital requirements have to calculate default probabilities (PDs) for all their obligors. Supervisors are mainly concerned about the credit risk being underestima ted. For high quality exposures or groups with an insufficient number of obligors, calculations based on historical data may not be sufficiently reliable due to infrequent or no observed defaults. In an effort to solve the problem of default data scarcity, modelling assumptions are made and to control the possibility of model risk, a high level of conservatism is applied. Banks, on the other hand, are more concerned about PDs that are too pessimistic, since this has an impact on their pricing and economic c apital. In small samples or where we have little or no defaults, the data provide very little information about the parameters of interest	posterior probability, correlation, prior probability, default rate, probability of default model, pd, proc step, data cleaning, sample, monte carlo, credit risk, cumulative frequency, maximum likelihood estimation, covariance, probability distribution	source	16
393	Visualizing Clinical Trial Data: Small Data, Big Insights	Michael Drutar, Elliot Inman	Data visualization is synonymous with big data, for which billions of records and millions of variables are analyzed simultaneously. But that does not mean that data scientists analyzing clinical trial data that include only thousands of records and hundreds of variables cannot take advantage of data visualization methodologies. This paper presents a comprehensive process for loading standard clinical trial data into SAS Visual Analytics, an interactive analytic solution. The process implements template reporting for a wide variety of point-and-click visualizations	adverse events, clinical trial, clinical data, data science	source	18
394	Visualizing Relationships and Connections in Complex Data Using Network Diagrams in SAS Visual Analytics	Stephen Overton, Ben Zenick	Network diagrams in SAS Visual Analytics help highlight relationships in complex data by enabling users to visually correlate entire populations of values based on how they relate to one another. Network diagrams are appealing because they enable an analyst to visualize large volumes and relationships of data and to assign multiple roles to represent key factors for analysis such as node size and color and linkage size and color. SAS Visual Analytics can overlay a network diagram on top of a spatial geographic map for an even more appealing visualization. This paper focuses specifically on how to prepare data for network diagrams and how to build network diagrams in SAS Visual Analytics. This paper provides two real-world examples illustrating how to visualize users and groups from SAS metadata and how banks can visualize transaction flow using network diagrams	raw data, left join, bank transaction, insurance risk score, proc sql	source zip	15
395	Visualizing Student Enrollment Trends Compared across Calendar Periods and Grouped by Categories with SAS Visual Analytics	Juan Olvera	Presentation	sas visual analytics, excel	source	4
396	Visualizing Your Big Data	Patricia S. Aanderud	Presentation	bar chart, line chart	source	5
397	Want an Early Picture of the Data Quality Status of Your Analysis Data? SAS Visual Analytics Shows You How	Gerhard Svolba	When you are analyzing your data and building your models, you often find out that the data cannot be used in the intended way. Systematic patterns, incomplete data, and inconsistencies from a business point of view are often the reason. You wish you could get a complete picture of the quality status of your data much earlier in the analytic lifecycle. SAS analytics tools like SAS Visual Analytics help you to profile and visualize the quality status of your data in an easy and powerful way. In this session, you learn advanced methods for analytic data quality profiling. You see case studies based on real-life data, where we look at time series data from a bird's-eye view and interactively profile GPS trackpoint data from a sail race	missing value, time series analysis, data cleaning, proc hpsplit, time series data, correlation, histogram, binary variable, proc timeseries, proc princomp, raw data, proc step, sample, regression analysis, line chart	source zip	19
398	What's New in SAS Data Management	Nancy A. Rausch, Mike Frost	The latest releases of SAS Data Integration Studio and DataFlux Data Management Platform provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can clean, standardize, transform, master, and manage your data. The latest features include capabilities for building complex job processes, new web-based development and job monitoring environments, enhanced ELT transformation capabilities, big data transformation capabilities for Hadoop, integration with the analytic platform provided by SAS LASR Analytic Server, enhanced features for lineage tracing and impact analysis, and new features for master data and metadata management. This paper provides an overview of the latest features of the products and includes use cases and examples for leveraging product capabilities	big data, sample, data cleaning, proc sql, data dictionary, data integration, hadoop, data governance	source	18
399	What's New in SAS Studio?	Mike Porter, Michael Monaco, Amy Peters	If you have not had a chance to explore SAS Studio yet, or if you're anxious to see what's new, this paper gives you an introduction to this new browser-based interface for SAS programmers and a peek at what's coming. With SAS Studio, you can access your data files, libraries, and existing program s, and you can write new programs while using SAS software behind the scenes. SAS Studio connects to a SAS server in order to process SAS programs. The SAS server can be a hosted server in a cloud environment, a server in your local environment, or a copy of SAS on your local machine. It's a comfortable environment for those used to the traditional SAS windowing environment (SAS Display Manager) but new features like a query window, process flow diagrams, and tasks have been added to appeal to traditional SAS Enterprise Guide users	sas studio, proc sql, sas programmer, proc step, sample, create table, sas enterprise guide, anova	source	12
400	When I'm 64-bit: How to Still Use 32-bit DLLs in Microsoft Windows	Richard D. Langston	Now that SAS users are moving to 64-bit Microsoft Windows platforms, some are discovering that vendor-supplied DLLs might still be 32-bit. Since 64-bit applications cannot use 32-bit DLLs, this would present serious technical issues. This paper explains how the MODULE routines in SAS can be used to call into 32-bit DLLs successfully, using new features added in SAS 9.3		source	3
401	When Two Are Better Than One: Fitting Two-Part Models Using SAS	Laura Kapitula	In many situations, an outcome of interest has a large number of zero outcomes and a group of nonzero outcomes that are discrete or highly skewed. For example, in modeling health care costs, some patients have zero costs, and the distribution of positive costs are often extremely right-skewed. When modeling charitable donations, many potential donors give nothing, and the majority of donations are relatively small with a few very large donors. In the analysis of count data, there are also times where ther e are more zeros than would be expected using standard methodology, or cases where the zeros might differ substantially than the non-zeros, such as number of cavities a patient has at a dentist appointment or number of children born to a mother. If data has such structure, and ordinary least squares methods are used, then predictions and estimation might be inaccurate. The two-part (two-stage) model gives us a flexible and useful modeling framework in many situations	homoscedasticity, proc step, regression analysis, data mining, poisson distribution, heteroscedasticity, negative binomial regression, logistic distribution, zero-inflated poisson, logistic regression, poisson regression, binomial distribution, proc logistic, binary variable, data cleaning, validation set	source	9
402	Where Did My Students Go?	Stephanie R. Thompson	Many freshmen leave their first college and go on to attend another institution. Some of these students are even successful in earning degrees elsewhere. As there is more focus on college graduation rates, this paper shows how the power of SAS can pull in data from many disparate sources, including the National Student Clearinghouse, to answer questions on the minds of many institutional researchers. How do we use the data to answer questions such as "What would my graduation rate be if these students graduated at my institution instead of at another one?", "What types of schools do students leave to attend?", and "Are there certain characteristics of students who leave, an d are they concentrated in certain programs?" The data-handling capabilities of SAS are perfect for this type of analysis, and this presentation walks you through the process	dataset, adam dataset	source	4
403	Why Aren't Exception Handling Routines Routine? Toward Reliably Robust Code through Increased Quality Standards in Base SAS	Troy Martin Hughes	A familiar adage in firefighting-If you can predict it, you can prevent it-rings true in many circles of risk management and accident prevention, including software development. If you can predict that a fire, however unlikely, someday might rage through a structure, it's prudent to install smoke detectors to facilitate its rapid discovery. Moreover, the combination of smoke detectors, fire alarms, sprinklers, fire retardant building materials, and rapid intervention may not prevent a fire from starting, but it can prevent the fire from spreading and facilitate its immediate and sometimes automatic extinguishment. Thus, as fire codes have grown to incorporate increasingly more restrictions and regulations and as fire suppression gear, tools, and tactics have continued to advance, even the harrowing business of firefighting has become more reliable, efficient, and predictable	exception handling, macro variable, automatic macro variable, data cleaning, sas base, macro put, data validation rules, sas log, quality control, proc sql, if-then-else, sysrc, proc sort, error message, quality assurance, sas programmer, proc report, quit, error, options statement, proc freq, macro function, macro definition, %if-%then-%else	source	17
404	Why Do People Choose to Drive Over Other Travel Modes? Modelling Driving Motivation with SAS	Jun Neoh	We demonstrated a method of using SAS to ease the interpretation of dimensions of a Multidimensional Scaling (MDS) model, a process which could be difficult without SAS. We used the `PROC LOGISTIC` function of SAS to identify the var iables which are strongly associated with each dimension, thus greatly aiding our interpretation procedure. In our demonstration, we examined the reasons that motivate people to choose to drive to work over other travel modes. We were able to extract the d rivers' motivation in four dimensions, namely responsibility for family, issues with public transportation, need to use the car for other tasks, and location. The paper is relevant to MDS users, who may find it useful to utilize SAS to analyze MDS results	proc step, proc logistic, logistic distribution, proc mds	source	8
405	Why Two Good SAS Programmers Are Better Than One Great SAS Programmer	Peter Timusk	Presentation	sas programmer, sas programming	source	4
406	Will You Smell Smoke When Your Data Is on Fire? The SAS Smoke Detector: A Scalable Quality Control Dashboard for Transactional and Persistent Data	Troy Martin Hughes	Smoke detectors operate by comparing actual air quality to expected air quality standards and immediately alerting occupants when smoke or particle levels exceed established thresholds. Just as rapid identification of smoke (i.e, poor air quality) can detect harmful fire and facilitate its early extinguishment, rapid detection of poor quality data can highlight data entry or ingestion errors, faulty logic, insufficient or inaccurate business rules, or process failure. Aspects of data quality -such as availability, completeness, correctness, and timeliness-together should be assessed against stated requirements that account for the scope, objective, and intended use of data products. A single outlier, an accidentally locked data set, or even subtle modifications to a data structure can cause a robust extract transform load (ETL) infrastructure to grind to a halt or produce invalid results	quality control, data cleaning, data validation rules, quality assurance, dictionary tables, proc freq, sas library, dataset, filename statement, character variable, macro parameter, numeric variable, sample, proc sql	source	25
407	Working with Panel Data: Extracting Value from Multiple Customer Observations	Bobby Gutierrez, Kenneth Sanford	Many retail and consumer packaged goods (CPG) companies are now keeping track of what their customers purchased in the past, often through some form of loyalty program. This record keeping is one example of how modern corporations are building data sets that have a panel structure, a data structure that is also pervasive in insurance and finance organizations. Panel data (sometimes called longitudinal data) can be thought of as the joining of cross-sectional and time series data. Panel data enable analysts to control for factors that cannot be considered by simple cross-sectional regression models that ignore the time dimension. These factors, which are unobserved by the modeler, might bias regression coefficients if they are ignored. This paper compares several methods of working with panel data in the `PANEL` procedure and discusses how you might benefit from using multiple observations for each customer. Sample code is available	proc panel, predictor variable, regression analysis, correlation	source zip	10
408	Working with PROC FEDSQL in SAS 9.4	Zabiulla Mohammed, Ganesh Kumar Gangarajula, Pradeep Reddy Kalakota	Working with multiple data sources from multiple vendors simultaneously in SAS had not been straight forward until the `PROC FEDSQL` was introduced in the SAS 9.4 release. FEDSQL, Federated Query Language is a vendor independent language which provides a common SQL syntax to communicate across multiple relational databases without having to worry about vendor specific SQL syntax. `PROC FEDSQL` is SAS implementation of the FEDSQL language. `PROC FEDSQL` enables us to write federated queries which can be used to perform joins on tables from different databases with a single query, without having to worry about loading the tables into SAS individually and combining them using `DATA STEP` and `PROC SQL` statements. The objective of this paper is to demonstrate the working of `PROC FEDSQL` to fetch data from multiple data sources such as Microsoft SQL Server database, MySQL database and a SAS dataset and run federated queries on all the data sources	proc sql, proc step, sql server, sas base, sas log, mysql	source	8
409	Yes, SAS Can Do! Manage External Files with SAS Programming	Justin Jia, Amanda Lin	Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations, and improve work efficiency significantly. Therefore it is needed under many circumstances to automate and standardize the file management processes through SAS programming. Compared with managing SAS files via `PROC DATASETS`, managing external files is a much more challenging task which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields	macro variable, sysrc, sas log, sas programming, macro definition, sas function, filename statement, put statement, dataset, character constant, error message, flat file, libname statement, sas library, options statement, txt, batch processing, catalog entry, data science, excel, proc step, work efficiency, macro quoting function	source	14
410	You Deserve ARRAYS: How to Be More Efficient Using SAS!	Kate Burnett-Isaacs	Everyone likes getting a raise, and using arrays in SAS can help you do just that! Using arrays simplifies processing, allowing for reading and analyzing of repetitive data with minimum coding. Improving the efficiency of your coding and in turn, your SAS productivity, is easier than you think! Arrays simplify coding by identifying a group of related variables that can then be referred to later in a `DATA STEP`. In this quick tip, you learn how to define an array using an ARRAY statement that establishes the array name, length, and elements. You also learn the criteria and limitations for the ARRAY na me, the requirements for array elements to be processed as a group, and how to call an array and specific array elements within a `DATA STEP`. This quick tip reveals useful functions and operators, such as the DIM function and using the OF operator within existing SAS functions, that make using arrays an efficient and productive way to process data	array, do loop, array bounds	source	5
411	You Say 'Day-ta', I Say 'Dat-a' - Measuring Coding Differences, Considering Integrity and Transferring Data Quickly	Jason R. Beene, Mary Federico Katz	We all know there are multiple ways to use SAS language components to generate the same values in datasets and output, e.g.: `DATA STEP` vs `PROC SQL`, If-Then-Else vs Format table conversions, `PROC MEANS` vs `PROC SQL` summarizations, just to name a few. However, do you ever compare those different ways to determine which are the most efficient in terms of CPU and elapsed time usage? Do you ever consider the time a programmer takes to develop or maintain code? In addition to determining the most efficient syntax, do you validate your resulting datasets? Do you ever check data values that need to remain the same after being processed by multiple steps and make sure they really don't change? We will share some of our best practices that have proven to save computer and human resources. We will also explain some data validation and comparison techniques that ensure data quality and integrity	proc step, proc sql, dataset, cpu time, proc compare, sas log, create table, frequency analysis, sas grid, proc means, proc contents, proc freq, group by, proc format, data cleaning, proc download, missing value, sas base, sql server, excel, sas programming	source	14
412	Your Database Can Do SAS Too!	Harry Droogendyk	How often have you pulled oodles of data out of the corporate data warehouse down into SAS for additional processing? Additional processing, sometimes thought to be uniquely SAS, might include FIRST. logic, cumulative totals, lag functionality, specialized summarization or advanced date manipulation. Using the Analytical (or OLAP) and Windowing functionality available in many databases (for example, in Teradata and IBM Netezza), all of this processing can be performed directly in the database without moving and reprocessing detail data unnecessarily. This presentation illustrates how to increase your coding and execution efficiency by using the database's power through your SAS environment	proc sql, group by, order by, data step, case reserve, loss reserve, time series analysis, proc step, data warehouse, lag function, proc append, excel, insurance claims	source	18
413	Your Top Ten SAS Middle-Tier Questions	Zhiyong Li, Mike Thorland	As SAS products become more web-oriented and sophisticated, SAS administrators face an increased challenge to manage their SAS middle-tier environments. They want to know the answers to important critical questions when planning, installing, configuring, deploying, and administrating their SAS products. They also need to meet the requirements of high performance, high availability, increased security, maintainability, and more. In this paper, we identify the most common and challenging questions that most of our administrators and customers have asked. These questions range across topics such as SAS middle-tier architecture, clustering, performance, security, and administration using SAS Environment Manager. The questions come from many sources such as technical support, consultants, and internal customer experience testing teams	memory usage, sas administrator	source	14

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