SCSUG 2017 Papers - Index

46 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Guru's Guide: Producing Section 508-Compliant Custom Reports with the Output Delivery System	Daniel O'Connor	Creating sophisticated, visually stunning reports is imperative in today's business environment, but is your fancy report really accessible to all? Let's explore some simple enhancements that were made in the fourth maintenance release of SAS 9.4 to Output Delivery System (`ODS`) layout and the Report Writing Interface that will truly empower you to accommodate people who use assistive technology. `ODS` now provides the tools for you to meet Section 508 compliance and to create an engaging experience for all who consume your reports	dataset, output delivery system	source	10
2	A Review of "Free" Massive Open Online Content (MOOC) for SAS Learners	Kirk Paul Lafler	Leading online providers are now offering SAS users with "free" access to content for learning how to use and program in SAS. This content is available to anyone in the form of massive open online content (or courses) (MOOC). Not only is all the content offered for "free", but it is designed with the distance learner in mind, empowering users to learn using a flexible and self- directed approach. As noted on Wikipedia.org, "A MOOC is an online course, content, or other resource aimed at unlimited participation and made available in an open access forum using the web." This presentation illustrates how anyone can access a wealth of learning technologies including comprehensive student notes, instructor lesson plans, hands-on exercises, PowerPoints, audio, webinars, and videos	sas base, sas studio, sample	source	16
3	A Ringside Seat: The ODS Excel Destination versus the ODS ExcelXP Tagset	Chevell T. Parker	The new and highly anticipated SAS Output Delivery System (`ODS`) destination for Microsoft `EXCEL` is finally here! Available as a production feature in the third maintenance release of SAS 9.4 (TS1M3), this new destination generates native `EXCEL` (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who uses the SAS System and Microsoft `EXCEL` to create reports. The discussion covers features and benefits of the new `EXCEL` destination, differences between the `EXCEL` destination and the older ExcelXP tagset, and functionality that exists in the ExcelXP tagset that is not available in the `EXCEL` destination. These topics are all illustrated with meaningful examples. The paper also explains how you can bridge the gap that exists as a result of differences in the functionality between the destination and the tagset	excel, format statement, proc step, output delivery system, ods excel, macro variable, proc import, datetime20., ods tagsets.excelxp, options statement, proc sgplot	source	32
4	A Study on H1B - VISA Petitions 2011-2016	Shubham Panat	The H1B Visa enables foreign skilled workers to stay in the US for up to six years and apply for permanent residency in the interim. Because this visa permits a foreign national to have "dual intent", there is no conflict when an H1B temporary worker applies for a gre en card. It is important for the U.S. Department of Labor to identify which employers are sending the most number of H1B Visa applications and what is the percentage share of the annual 85,000 visa cap for the employers with most applications. At the same time, figure out the most common job title, examine the data science jobs, and their relative wages for which the application is coming in larger frequency. This study provides information about the employment gaps within the U.S., which are filled by foreign workers. This gives an idea as to which areas the U.S. government should emphasize to encourage domestic students to develop more local workforce in those job domains for the future	data science, machine learning	source	12
5	An Introduction to PROC REPORT	Kirk Paul Lafler	SAS users often need to create and deliver quality custom reports and specialized output for management, end users, and customers. The SAS System provides users with the `REPORT` procedure, a "canned" Base-SAS procedure, for producing quick and formatted detail and summary results. This presentation is designed for users who have no formal experience working with the `REPORT` procedure. Attendees learn the basic `PROC REPORT` syntax using the COLUMN, DEFINE, other optional statements, and `PROCEDURE OPTIONS` to produc e quality output; explore basic syntax to produce basic reports; compute subtotals and totals at the end of a report using a COMPUTE Block; calculate percentages; produce statistics for an analysis variable; apply conditional logic to control summary outpu t rows; and enhance the appearance of output results with basic Output Delivery System (`ODS`) techniques	proc report, proc step, excel, title statement	source	21
6	Analysis of the effects of financial parameters on GDP and poverty using panel data analysis.	Akshay Arora		net interest margin, proc hpsplit, response variable, regression analysis, proc reg, missing value, data cleaning, data mining	source	13
7	Analysis on EpiPen Tweets during Crisis Situations	Goutam Chakraborty, Miriam McGaugh, Josh Weiner	The impact of social media on businesses is huge as the number of people sharing their unbiased opinions on platforms like Twitter has increased drastically over the past few years. Both good and bad publicity can spread to the masses in a matter of few seconds. Mylan N.V., an American global pharmaceutical company that acquired the right to market the EpiPen is one such company which has been appearing on the Social Media from the past one year. People tweeted their concerns when the price of EpiPens has been increased drastically in 2016, when more than 80,000 EpiPens have been recalled in March 2017 across multiple countries and in April 2017 when Sanofi filed antitrust lawsuit against Mylan over EpiPen[5]. The primary objective of this research is to know about the attitude of people towards the company and the industry and if prior reputation of the company (post price hike) influenced how people reacted to EpiPen product recall and lawsuit against EpiPen	data mining, dataset	source	14
8	Analyzing Theft Occurrences in Chicago Using SAS Enterprise Miner 14.1	Shikha Prasad, Kunal Parekh, Goutam Chakraborty, Miriam McGaugh	Chicago is one of the big cities in the United States that has witnessed a dramatic change in the crime rates in the past few decades. The objective of this paper is to find specific patterns for crimes such as larceny and motor vehicle theft versus other crimes that include battery, homicide, criminal damage, assault, etc. using Base SAS 9.4 and SAS Enterprise Miner 14.1. The purpose is to identify locations in the city that have the most frequent instances of these two types of thefts reported since 2012	missing value, proc hpsplit, data mining, regression analysis, data cleaning	source	10
9	Apple or Samsung? Which Phone Should I Buy? Sentiment Analysis Utilizing Customer Reviews on Unlocked iPhone and Samsung Galaxy	Rosie Nguyen, Nandan Moza, Goutam Chakraborty, Miriam McGaugh	The mobile phone market in the USA is usually regarded as the dominant segment of the world market because of its diversity and fast-paced technology development. A survey shows that nearly 200 million persons in the United States own a smartphone. The top two brands are Apple and Samsung with roughly 40% and 30% market share, respectively. A large number of sales of these t wo brands were made on Amazon.com, a popular e - commerce website with thousands of reviews for each brand. However, a potential buyer would find it impossible to review all of these customer reviews to decide which brand to buy. Therefore, consumers are very likely to refer to a small percentage of the comments as they attempt to make an informed decision. Our goal was to aim to leverage consumer opinion on Amazon's website to understand what features generated more positive feedback, and what aspects dre w more positive impression after purchasing i Phone or Samsung Galaxy models	data mining, parsing, proc freq, sas enterprise guide	source	14
10	Arrays: Know When to Hold Them, Know When to Fold Them	Patricia Hettinger	Sometimes you have to process data as a group, with one item of the group influencing how the other items are processed (think draw poker or bowling scores). This is often best handled by using SAS variable arrays. This paper goes through a complete application from loading the arrays to using one to influence other variables and tips on testing and validation	error message, temporary array, array	source	14
11	Building a Better Dashboard Using SAS Base Software	Kirk Paul Lafler, Joshua M. Horstman, Roger D. Muller	Organizations around the world develop business intelligence dashboards, sometimes referred to as enterprise dashboards, to display the current status of "point-in-time" metrics and key performance indicators. Effectively designed dashboards extract real-time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, performance scorecards and other essential content on a single screen. This presentation explores the basic rules for "good" dashboard design, the metrics frequently used in dashboards, and the use of best practice programming techniques in the design of highly interactive, filterable, and drill-down dashboards using Base-SAS software	proc step, bar chart, vertical bar chart, proc format, proc print, if-then-else, ods html, proc template, dataset, footnote statement, horizontal bar chart, order by, proc sgpanel, proc sql, sas base, user-defined format, histogram	source	21
12	Classifying and Predicting Spam Messages Using Text Mining in SAS Enterprise Miner	Venkata Kondamudi, Sai Mounika, Balamurugan Mohan	In this technologically advanced digital world, identifying a spam message is of extreme importance. Spam messages are generally unsolicited and unwanted messages and when accessed can trap people in scam subscriptions that might infect their devices with malicious software. Sometimes this can be even more annoying to the recipient because, unlike in email, some recipients may be charged a fee for every message received. The dataset used for this analysis is a collection of 6,927 Spam and Ham (General conversation, anti-spam) messages that include 5,574 (747 spam, 4,827 ham) English messages from UCI Machine Learning Repository and a corpus of 1,353 spam messages from Dublin Institute of Technology (DIT). This paper motivates work on identifying clusters of high frequency spam and ham words. A classification model, which can classify and predict the messages as spam and ham based on the rules built by the text builder node, is discussed	parsing, data mining, data dictionary, response variable, proc freq, data cleaning	source	14
13	Data Analysis and Storytelling - Communicating Analytical Results with Clarity, Precision and Efficiency	Kirk Paul Lafler	The data analysis process involves the gathering and collection, cleansing, transforming, and modeling of data from various sources. The purpose is to discover, evaluate, understand and derive useful information from the data to support decision-making. Unfortunately, and all too often, data analysts omit a very crucial step - the development of a narrative, or story, of the data analysis process and outcome. This omission not only fails to bring context, insight and interpretation of the data analysis results to stakeholders, it neglects to bring meaning, relevance and interest to the "key" points of the data analysis results. This presentation describes the importance, considerations and steps needed in developing a compelling narrative, along with the necessary visual analytics, to communicate a convincing point-of-view to help persuade others to understand the complexities associated with the data analysis results	proc step, data science, data visualization, proc sgplot, vertical bar chart, bar chart, histogram, proc freq, proc sort, sas base, proc means	source	10
14	Differentiate Yourself	Kirk Paul Lafler	Today's employment and business marketplace is highly competitive. As a result, it is necessary for SAS professionals to differentiate themselves from the competition. Success depends on a number of factors, including positioning yourself with the necessary technical skills in relation to the competition. This presentation illustrates how SAS professionals can acquire a wealth of knowledge and enhance their skills by accessing valuable and free web content related to SAS. With the aid of a web browser and an Internet connection, anyone can access published PDF papers, Microsoft Word documents, Microsoft PowerPoint presentations, student notes, instructor lesson plans, hands-on exercises, webinars, audios, videos, a comprehensive technical support website maintained by SAS, and more to acquire the essential expertise that is needed to cut through all the marketplace noise and begin differentiating yourself to secure desirable opportunities with employers and clients	sas programmer, sas consultant	source	20
15	Domo Arigato SAS: Turn Your PC Into A BASE SAS Robot	Michael McCarthy	Data can take a long and winding path before graduating to become a SAS data set. Using the native OS can frequently be an easier way to navigate this journey. This paper is written for BASE SAS programmers who want to control their PC from the DOS command prompt using the "CALL SYSTEM ()" command. A method to command DOS from a SAS script is discussed, and a step-by-step example demonstrating file download from a web server and subsequent file operations from a BASE SAS macro is presented	sas base, macro variable	source	4
16	Don't Fear The Network: Beat A Slow Network With Data Content Maps	Michael McCarthy	While SAS programmers can improve benchmark speeds with better algorithms and faster computers, we cannot deliver results any faster than our network will allow. This can become a serious issue when we perform queries involving large data sets across several networked computers. Fortunately, there is a simple strategy to overcome this roadblock. A method to reduce the number of network data transactions is described, and a step-by-step example using BASE SAS to create a Data Content Map is presented	sas base, create table, proc step, macro definition, call execute, index, proc sql, dataset, raw data, macro variable	source	12
17	Enabling Transitions of Care with SAS	Christopher W. Schacherer	As changes in healthcare payment models continue to evolve, provider organizations face a constant need for a strong data management and analytics capability to help them respond to shifting measurement and analysis requirements. Population Health Management (PHM) software and Business Intelligence (BI) solutions provide many of the required capabilities to identify gaps in care and measure clinical and financial performance. However, even with industry-leading packaged software solutions in place, custom data management solutions are often needed to respond quickly to specific requirements. SAS software provides a wide array of functionality to fill these gaps. The current work describes how the `FILENAME` statement, `PROC SQL`, and `DATA STEP` were used to help providers in one accountable care organization (ACO) quickly identify when their patients were discharged from area hospitals and emergency departments so they could provide appropriate follow-up care	filename statement, macro variable, proc step, dataset, data governance, macro put, proc sql	source	12
18	Enhancement of Clinical Decision Making Using Predictive Modeling	Archana Chinnaswamy	With the mandate of Electronic Health Records (EHR), there is an abundance of data available for every member in the healthcare supply chain. One big question that has remained is, "How effectively can we transform this data into a valuable resource for improving the quality and cost of healthcare? "The problem is not inadequacy of health data, but lack of a quantifiable health assessment tool, that will not only assess a patient's health status but also predict the patient's future visits to a hospital and potential future patient costs. This paper aims to propose a quantifiable ranking tool based on pred ictive modeling to aid in improved clinical decision making. Data of over 300,000 AI/AN patients were analysed, cleaned, and transformed to predict future hospital visits and hospital costs. Using predicted numbers and segmentation techniques, patients wer e categorized into different risk levels. This information can help health- care providers provide quality healthcare	proc hpsplit, data mining, regression analysis	source	9
19	From Researcher to Programmer: 5 SAS Tips I Wished I Knew Then	Crystal Carel	Having cross the spectrum from an epidemiologist & researcher (where ad hoc is life & focusing on research) to a SAS programmer (writing reusable code for automation, meaning no manual interventions), I have learned a few things that I wished I knew then as a researcher to not only be a better SAS programmer but also to save time & effort as a researcher by having well organized, accurate code (that I didn't accidentally remove) & code that will work if ran again on another date. This paper will present 5 SAS tips that are common practice among SAS programmers. I will provide researchers who use SAS with tips that are handy, useful, & provide code (where applicable) to be able to try out at home. Using the tips provided will make any SAS programmer smile when they get presented with your code (not guaranteed, but your results should not vary by using these tips)	macro variable, sas log, macro put	source	5
20	Fuzzy Matching Programming Techniques Using SAS Software	Kirk Paul Lafler, Stephen B. Sloan	Data comes in all forms, shapes, sizes and complexities. Stored in files and data sets, SAS users across ind ustries know all too well that data can be, and often is, problematic and plagued with a variety of issues. When unique and reliable identifiers are available, users routinely are able to match records from two or more data sets using merge, join, and/or hash programming techniques without problem. But, what happens when a unique identifier, referred to as the key, is not reliable or does not exist	proc step, proc sql, proc sort, character variable, proc freq, proc contents, order by, missing value, numeric variable, sas function, data merging, where statement, nominal variable, create table, data transformation, proc print, proc transpose, character functions, data cleaning	source	25
21	Google Searches for the Inquiring SAS User	Kirk Paul Lafler	Google (www.google.com) is the world's most popular and widely-used search engine. As the premier search tool on the Internet today, SAS users frequently need to identify and locate SAS-related content wherever and in whatever form it resides. This paper provides insights into how Google works and illustrates numerous search tips and techniques for finding articles of interest, reference works, information tools, directories, PDFs, images, current news stories, user groups, and more to get search results quickly and easily		source	6
22	I Didn't Know You Could Do That! Revelations from the Advanced SAS Certification Exam	Patricia Hettinger	Many people have extreme test anxiety. But studying for a test can give you a lot of insight into the subject matter you may not have had before. This is certainly true of the SAS certification exams. Even If you have been a successful SAS professional, there is still a lot to be gained from certification beyond simple credentials. This paper details some features of SAS that were a complete revelation to the author and many be of service to you in your own daily work	proc sql, proc step, where statement, cpu time, view, dataset, proc print, full join, macro definition	source	9
23	Life Expectancy Tables - Getting SAS to run the hard math	Anna Vincent, Suting Zheng	Life expectancy (LE) tables are statistical tools typically used to portray the expectation of life at various ages and are a fundamental tool in survival analysis. They also provide information on the number of individuals who survive to various ages, median age at death, age-specific death rates, and the probability of dying at certain ages. LE at birth is the most frequently cited LE table statistic. The Texas Department of State Health Services (DSHS) creates LE for publication in the Vital Statistics Annual Report every year. Previously, these tables were constructed in a multi-step process using detailed Python and Java script. In this paper we provide a general framework for building LE tables using SAS	life table, sas syntax	source	8
24	Make it a Date!: Setting up a Master Date View	Crystal Carel	Dates are the basis of all pain and gain in programming. In my experience, working with beginning to seasoned programmers, dates are the number one item that make (and break) processes. Whether developing ETL or reporting, almost all data management and sourcing involves dates. Thus, dates have been a source of difficulty when programming for automation. This doesn't have to be the case though! Having a date view that is prepopulated with every variation of dates you may need while reporting helps a programmer (or team) become more effective while eliminating data manipulation that can halt processes. While this all may seem complicated, it is easier done than said! This paper will provide a SAS programmer instructions with the code needed to set up, create, and have their own (or shared) date view to drive data processes without having to create hard coded dates or date macros every session	proc sql, date variable, do loop, proc step	source	10
25	One-to-One, One-to-Many, and Many-to-Many Joins Using PROC SQL	Charu Shankar, Kirk Paul Lafler	A powerful and essential `PROC SQL` programming technique that all SAS users should understand, and be comfortable performing, is the process of joining (or combining) two or more tables of data. This paper and presentation describes and illustrates the join process, including what a join is, exploration of one-to-one, one-to-many, and many-to-many data relationships, identifying a primary key (or unique identifier), special preparation requirements for each table being specified in a join, and popular join techniques available to SAS users. To demonstrate the power of the join process, examples of conventional (symmetrical matching) and unconventional (asymmetrical left, right and full matching) using `PROC SQL` join programming techniques are illustrated	proc sql, data merging, where statement, inner join, proc step, full join, right join	source	19
26	Prediction of Used Cars? Prices by Using SAS EM	Jaideep Muley, Vijay Kumar Movva		regression analysis, proc hpsplit, class variable, normal distribution, data mining, response variable, correlation, data cleaning	source	12
27	Predictive Analysis for Classifying Type 2 Diabetes Patients Using SAS Enterprise Miner 14.1 and SAS Enterprise Guide 7.1	Saurabh Sanjayrao Kokad	In the past few years, the healthcare industry has produced massive amounts of data. Instead of making hard copies of the records, digitization of the records and computerized approach helped to produces these large data. The healthcare industry is moving from only reporting facts out of this data to discovery of the insights and predicting prospective trends by studying patterns in the past. This paper is summary of finding the patt ern between diabetic patient demographics, classifying them into Primary, Secondary and Tertiary groups based on existing medical standard testing results and predicting the primary, secondary and tertiary classification of the patient with different demographical and standard health variables using theoretical classification as a target . A dataset containing 900,000 records with various 12 variables was obtai ned from Center for Health System Innovation (CHSI), Oklahoma State University(OSU)	proc hpsplit, sas enterprise guide, data mining, sample, data cleaning, interval variable	source	10
28	Pruning the SASLOG - Digging into the Roots of NOTEs, WARNINGs, and ERRORs	Andrew T. Kuligowski	You've sat through constant design meetings. You've endured countless requests for "just one more little change". You even managed to find a creative solution to that nagging technical problem. But, you persevered, and despite all of the obsta cles, you've managed to eliminate the final syntax error in your newest SAS routine. Time to sit back and relax -- uh, not quite ... The primary focus of this presentation will be on techniques to ensure comprehension of your inpu t data. We will look at several messages that are often found in the `SASLOG`, such as: NOTE: `MERGE` statement has more than one data set with repeats of BY values. that imply that there may be gaps in your knowledge of your data! Special emphasis will be placed on the use of ad -hoc queries to assist in finding data anomalies that can cause problems with your SAS code	input statement, numeric variable, proc step, merge statement, out=, index, data cleaning, infile statement, proc means, dataset, e., sample, put statement	source	10
29	Red Rover, Red Rover, Send Data Right Over: Exploring External Geographic Data Sources with SAS	Louise S. Hadden	The intrepid Mars Rovers have inspired awe and Curiousity - and dreams of mapping Mars using `SAS/GRAPH`. This presentation will demonstrate how to import SHP file data (using `PROC MAPIMPORT`) from sources other than SAS and GfK to produce useful (and sometimes creative) maps. Examples will include mapping neighborhoods, ZCTA5 areas, postal codes and of course, Mars. Products used are Base SAS and `SAS/GRAPH`. SAS programmers of any skill level will benefit from this presentation	proc step, proc format, cdisc	source	9
30	Removing Duplicates Using SAS	Kirk Paul Lafler	We live in a world of data - small data, big data, and data in every conceivable size between small and big. In today's world data finds its way into our lives wherever we are. We talk about data, create data, read data, transmit data, receive data, and save data constantly during any given hour in a day, and we still want and need more. So, we collect even more data at work, in meetings, at home, using our smartphones, in emails, in voice messages, sifting through financial reports, analyzing profits and losses, watching streaming videos, playing computer games, comparing sports teams and favorite players, and countless other ways. Data is growing and being collected at such astounding rates all in the hopes of being able to better understand the world around us	proc step, proc sort, proc sql, proc means, group by	source	9
31	Risk Based Monitoring (RBM) with SAS Visual Analytics	Prasoon Sangwan, Piyush K. Singh, Faraz Khan	Risk based monitoring is an approach defined by regulatory services which not only helps in oversight of clinical trial to ensure efficient conduction of trial. It not only prevents the risks to data quality, subject safety etc. but also tracks the progress of the study as per the plan. SAS Visual Analytics plays a vital role in generating graphs and figures for monitoring the data and easily identifying the area of concern. In this paper, we will discuss how SAS Visual Analytics can help us generate the major key performance Indicators like Subject enrolment, data quality and management, subject safety and compliance etc. easily to perform central monitoring	adverse events, bar chart, clinical trial, data cleaning, randomization	source	10
32	SAS Grid Job Search Performance	Piyush K. Singh, Prasoon Sangwan, Ghiyasuddin Mohammed Faraz Khan	Have you ever tried to find the job execution information in SAS Grid? Though, you can use SASGSUB to check the job status but if you really need detailed information then you can use LSF command "bacct". It provides the detailed information about total job execution but it is quiet slow and usually takes around 15 sec or more to search for jobs executed by a single user. If Grid platform has more than 100 users, which is quiet common in any medium to big sized organization, then bacct may take more than 25 minutes (100*15 sec) to list jobs for all users. If there are around 500 to 1 000 users, then bacct may take hours to bring the list of executed jobs. This paper explains the method to extract the job execution reports (or job search performance) for 1000s of users in just few minutes. It also explains how we can automate the entire process and we can create different kind of reports in a few minutes	cpu time, sas grid, sample, sas management console, excel	source	7
33	SAS vs Python - How Do They Compare?	Daniel R. Bretheim	When you see an interesting data set, report, or figure, do you wonder what it would take to replicate those outputs in SAS? This paper will do just that, by using SAS to recreate a complex graph that was originally generated by Python. A key concept for understanding this comparison is that the starting point is the Python code. The paper will associate snippets of Python with the corresponding SAS statements, attempting a reasonable apples-to-apples comparison. In other words, the resulting SAS code will not necessarily represent how it would have been written if we had started with SAS rather than Python. The objective of the paper is to show SAS programmers how SAS code lines up with the widely used Python language. We will start with a brief summary of how SAS and Python compare across several environmental dimensions	create table, date variable, proc step, proc sql, scatter plot	source	18
34	Sentiment Analysis on YouTube Movie Trailer Comments to Determine the Impact on Box-Office Earnings	Rishanki Jain	The video-sharing website YouTube encourages interaction between its users via the provision of a user comments facility. This was originally envisaged as a way for viewers to provide information about and reactions to videos, but is employed for other communicative purposes including sharing ideas, paying tributes, social networking, and question answering. This study seeks to examine and categorize the types of comments made by YouTube users to understand how the sentiments of users can impact the first day revenue	data mining, parsing, data cleaning	source	9
35	Snail Mail to Emails: Generating Auto-Emails from SAS with Attachments	Crystal Carel	Do you get tired of constantly creating new emails each and every time you run a report, frantically searching for the reports, attaching said reports, and writing emails while thinking ther e has to be a better way? Then have I got some code to share with you! This session will provide you with code to flee your old ways of emailing data and reports and instead have you set up your SAS code to send an email to your recipients that has attached the most current file(s) each and every time the code is ran without having to manually do anything after you run your SAS code. This session will provide a SAS programmer with instruction on ho w to create your own email that is in a macro that is based on your current report	macro variable, macro call	source	7
36	Text Mining on Donor's Conversation with Solicitors	Harsh Gupta	Solicitors pursue alumni all the time into giving back to the school. Solicitors contact alumni via all the means and note down every conversation they have with them, which are summarized conversations with personal notes added. The objective of this paper is to analyze the text to uncover insights and in the end do predictive modelling. Using text mining nodes in SAS Enterprise Miner 14.1, we will perform text analytics on a database obtained from a university foundation that contained 38,000 observations. In text analytics, text clustering was conducted and meaningful clusters were obtained and utilized for donor segmentation. We also used text topic node. The rule builder node was used to find key words that were associated with a donation. Predictive modelling was conducted on text data and text/ numerical combinations and various models were compared. The numeric variables were three internal ratings, gender, degree, school, marital status and state	numeric variable, character variable, data mining, data cleaning, parsing, regression analysis	source	12
37	The Cox Hazard Model for Claims Data	Samuel Berestizhevsky, Tanya Kolosova	General insurance protects individuals and organizations from financial losses due to damages or legal liabilities. It allows policyholders to exchange the risk of a large loss for the certainty of smaller periodic payments of premiums. Insurers allocate the premiums dollars into investment and claims payments. As it is for an insurer to manage its investment portfolio, it is equally important to manage its claims portfolio. Claim management is the analytics of insurance costs. It requires applying statistical techniques in the analysis and interpretation of the claims data. In the data-driven industry of general insurance, claim management provides useful insights for insurers to make better business decisions. The central piece of claim management is claims modeling. Two strategies are commonly used by insurers to analyze claims: the two-part approach that decomposes claims cost into frequency and severity components, and the pure premium approach that uses the Tweedie distribution	hazard function, proc phreg, survival function, hazard ratio, proportional hazards assumption, time-dependent covariate, workers compensation, survival analysis, loss event, insurance claims, markov chain, prior probability, sample, posterior probability, proc step	source	17
38	The Good, The Bad, and The Ugly	Toby Dunn, Kirk Paul Lafler	The SAS System has all the tools users need to read data from a variety of external sources. This has been, perhaps, one of the most important and powerful features since its introduction in the mid-1970s. The cornerstone of this power begins with the INFILE and INPUT statements, the use of a single- and double-trailing @ sign, and the ability to read data using a predictable form or pattern. This paper will provide insights into the INFILE statement, the various styles of INPUT statements, and provide numerous examples of how data can be read into SAS with the `DATA STEP`	input statement, raw data, infile statement, dataset, missing value, sas log, trailing @	source	12
39	The True Price for Your House	Mettilda Kaimathuruth, Goutam Chakraborty		anova, regression analysis, neural network, outlier	source	15
40	Three Great Reasons to Use the SAS ODS Excel Destination	Kirk Paul Lafler	SAS software is the "gold" standard for robust and reliable data access, manipulation, analytics, analysis, reporting, and data discovery. Microsoft `EXCEL` is the most widely used software in the world. This paper and presentation introduces the Output Delivery System (`ODS`) `EXCEL` destination; and three great reasons to use the SAS `ODS` `EXCEL` destination; along with the application and construction of SAS and `ODS` `EXCEL` code to share data, tables, statistics, images, reports, and results to create exciting and robust Microsoft `EXCEL` files and workbooks. Keywords SAS, `ODS`, `ODS` `EXCEL`, `EXCEL` workbooks, styles, images	excel, proc step, dataset, proc export, proc univariate, proc import, output delivery system, proc print, ods html, sas output, libname statement, bar chart, libname engine, ods pdf, proc template, proc sql	source	26
41	Tips, Techniques and Examples for Modernizing SAS Legacy Application Program Code	Kirk Paul Lafler, Clark Roberts	Whether you are a novice or experienced SAS programmer with responsibility for the support of your organization's legacy applications, programs and code, assistance is available to help modernize and streamline code for the 21st century and beyond. This paper and presentation explores the available constructs, statements, functions, algorithms, operators, methods, expressions, programming techniques and approaches users have to update and modernize legacy applications, programs and code first introduced as far back as the 1970's. Attendees learn how to use the `SCAPROC` procedure - the SAS code analyzer - to analyze metadata about the contents of SAS code, and to streamline, scale and modernize code constructs, statements, functions, algorithms, and legacy applications and program code. Table of Contents	proc step, if-then-else, proc sql	source	50
42	Top Ten SAS Performance Tuning Techniques	Kirk Paul Lafler	The Base-SAS software provides users with many choices for accessing, manipulating, analyzing, and processing data and results. Partly due to the power offered by the SAS software and the size of data sources, many application developers and end-users are in need of guidelines for more efficient use. This presentation highlights my personal top ten list of performance tuning techniques for SAS users to apply in their applications. Attendees learn DATA and `PROC STEP` language statements and options that can help conserve CPU, I/O, data storage, and memory resources while accomplishing tasks involving processing, sorting, grouping, joining (merging), and summarizing data	dataset, proc step, proc sql, cpu time, options statement, where statement, dictionary tables	source	12
43	Use Machine Learning to Discover Your Rules	David R. Duling	Machine learning is not just for data scientists. Business analysts can use machine learning to discover rules from historical decision data or from historical performance data. Decision tree and logistic regression scorecard learning are available for standard data tables, and associations' analysis is available for transactional event tables. These rules can be edited and optimized for changing business conditions and policies, and then deployed into automated decision making systems. Users will see demonstrations using real data and learn how to apply machine learning to business problems	data validation rules, machine learning, proc hpsplit, sample, response variable, flag variable, logistic regression, logistic distribution, regression analysis, data mining, score code	source	10
44	Using SAS Enterprise Miner for Categorization of Customer Comments to Improve Services at the U.S. Postal Service	Olayemi Olatunji	Delivering high-quality service and providing excellent customer experiences are performance outcome goals the U.S. Postal Service has established to measure corporate strategy success and continuous improvement efforts. Social media has opened the door for customer engagement and decision making. With the help of Twitter, Facebook, and Yelp, government agencies are more informed about how customers feel about their service and experience. Using Yelp data we will text mine comments about U.S. Postal Service customer service, retail service, mail delivery, and facility services using SAS Text Miner of SAS Enterprise Miner 7.1. The aim of this paper is to provide ways to categorize consumer comments regarding U.S. Postal Service services to improve the customer experiences at stations	parsing, data mining	source	8
45	Which smartphone would you choose?	Mohana Krishna Chaitanya Korlepara, Miriam McGaugh, Goutam Chakraborty		parsing, customer satisfaction, data mining	source	15
46	Zero My Hero!	Anna Vincent	This paper will demonstrate how to put zeros, null, or blank values into a `PROC REPORT` or a `PROC TABULATE`. When you use a plain `PROC REPORT` or a `PROC TABULATE` for some crosstabs, if a value is zero or blank, that value will not show by default. What if you need it to show?	proc step, proc report, proc tabulate, class variable, proc format, missing value	source	8

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