MWSUG 2019 Papers - Index

71 papers. Source: MWSUG (Midwest SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	10 Cool Things You Can Do in a DATA STEP	Stephanie R. Thompson	A look at some interesting things you can do in a `DATA STEP` that would be harder to do in other procedures or that are just plain interesting. Different aspects of utilizing the PDV, joins, editing a file without bringing it in to SAS, automatic variables, and a bit of what to do with DATA `_NULL_` a re some of the highlights. See the possibilities in the `DATA STEP`!	set statement, macro variable	6
2	3 ways to get Pretty Excel-Style Tables: PROC REPORT, PROC TABULATE, and Help from SAS Enterprise Guide	Brooke Ellen Delgoffe	SAS programmers with a variety of experience levels may be asked to provide tabular or summary data in an `EXCEL`-style format (stacked headers, colored headers, bolded total lines, etc.). This paper explores 3 different ways to produce and export Microsoft `EXCEL`-style tables into Output Delivery System (`ODS`) locations or the results window using SAS 9.4 or SAS Enterprise Guide 7.15. Addition of `ODS` style elements will help readers to apply aesthetically pleasing colors and formatting to their output. The use of Enterprise Guide will help new users of SAS perform these tasks with little to no SAS programming knowledge, while helping more versed SAS programmers utilize pre-written code as a starting point. Exploration of how to use the `SQL` procedure in combination with the `REPORT` procedure to display distinct counts and other uniquely formatted summary statistics will give programmers a succinct way to display summary statistics in the presence of repeated measurement	proc step, proc report, excel, proc tabulate, sas enterprise guide, proc sql, class variable, group by, proc means, sas programmer	16
3	A Visual Step-by-step Approach to Converting an RTF File to an Excel File	Kirk Paul Lafler	Rich Text Format (RTF) files incorporate basic typographical styling and word processing features in a standardized document that many programs and applications are able to read. In today's high-tech arena sometimes the contents of an RTF file needs to be viewed as, and even converted to, an `EXCEL` file. You would think that since both RTF and `EXCEL` are Microsoft standards that this would be a simple process to achieve, but you may be surprised to find out that is not the case. Learn about several "free" web-based and online applications as well as traditional SAS-based programming techniques that can be used to convert an RTF file to an `EXCEL` file	excel, txt	5
4	Abstracting and Automating Hierarchical Data Models: Leveraging the SAS FORMAT Procedure CNTLIN Option To Build Dynamic Formats That Clean, Convert, and Categorize Data	Troy Martin Hughes	The SAS `FORMAT` procedure "creates user -specified formats and informats for variables." i In other words, FORMAT defines data models that transform (and sometimes bin) prescribed values (or value ranges, in the case of numeric data) into new values. SAS formats facilitate multiple objectives of data governance, including data cleaning, the identification of outliers or new values, entity resolution, and data visualization, and can even be used to query or join lookup tables. SAS formats are often hardcoded i nto SAS software, but where data models are fluid, formats are best defined within control files outside of software. This modularity and data independence-the separation of data models from the programs that utilize them-allows SAS developers to build and maintain SAS software independently while domain subject matter experts (SMEs) separately build and maintain the underlying data models	format statement, proc format, data standard, proc report, dataset, exception handling, macro parameter, macro variable, proc step, master data management, data governance, data transformation, macro definition, proc freq, data cleaning	20
5	Are you Ready? Preparing and Planning to Make the Most of your Conference Experience	Richann J. Watson, Louise S. Hadden	Whether you are a first-time conference attendee or an experienced conference attendee, this paper can help you in getting the most out of your conference experience. As long-standing conference attendees and volunteers, we have found that there are some things that people just don't think about when planning their conference attendance. In this paper we will discuss helpful tips such as making the appropriate travel arrangements, what to bring, networking and meeting up with friends and colleagues, and how to prepare for your role at the conference. We will also discuss maintaining a workplace presence with your paying job while at the conference	problem solving, likelihood function	15
6	Badge in Batch with Honeybadger: Generating Conference Badges with Quick Response (QR) Codes Containing Virtual Contact Cards (vCards) for Automatic Smart Phone Contact List Upload	Troy Martin Hughes	Quick Response (QR) codes are widely used to encode information such as uniform record locators (URLs) for websites, flight passenger data on airline tickets, attendee information on concert tickets, or product information on product packaging. The prolife ration of QR codes is due in part to the broad dissemination of smart phones and the accessibility of free smart phone applications that scan QR codes. With the ease of QR code scanning has come another common QR code usage -the identification of conference attendees. Conference badges, emblazoned with attendee-specific QR codes, can communicate attendee		13
7	Base SAS & SAS Enterprise Guide Automate Your SAS World with Dynamic Code ~ Forwards & Backwards	Kent Phelps, Ronda Phelps	Communication is the basic foundation of all relationships , including our SAS relationship with the server, PC, or mainframe. To communicate more efficiently ~ and to increasingly automate your SAS world ~ you will want to learn how to transform static code into dynamic code that automatically re-creates the static code, and then executes the re-created static code automatically. Our workshop highlights the powerful partnership that occurs when dynamic code is creatively combined with a dynamic `FILENAME` statement, the SET INDSNAME option, a macro variable, and the CALL EXECUTE command within one SAS Enterprise Guide Program node. You have the exciting opportunity to learn how to design dynamic code forwards and backwards to re-create static code while automatically chang ing the year ~ as 1,574 time-consuming manual steps are amazingly replaced with only one time -saving dynamic automated step	filename statement, call execute, sas enterprise guide	23
8	Breaking Human Trafficking Network: An Analytics Approach	Raj Laxmi Prakash, Miriam McGaugh	Labor migration, illegal sex workers, child trafficking and many more impact our society in many ways. The need for developing a tool to discover and track human trafficking is extremely important and pulls the attention of many researchers. This exploratory paper describes the comprehensive analysis of the role of online classified advertisements in facilitati ng sex trafficking specifically and explores technological innovations to combat the increasing network of human traffickers. With the growth of internet and social media, human trafficking networks are spreading from the ease of communication. On websites such as Backpage.com, different online advertisements are posted to lure men, women, teens and children. These ads are used for selling as well as recruiting potential victims by manipulations and false promises of a job etc. Traffickers have become more sophisticated in their methods resulting in being seemingly untraceable and hiding their identities	proc freq, data wrangling, correlation, sas enterprise guide	16
9	Classifying Risk in Life Insurance using Predictive Analytics	Sai Gopi Krishna Govindarajula	Ever wonder how many companies offer life insurance? There are more than 600 companies in the US alone offering life insurance policies. Insurance companies perform an underwriting process to assess the risk of life insurance applicants and then price the policies if approved. Those underwriters gather extensive information about applicants, which include extensive health histories, to classify risk profiles. The process of collecting existing data for the risk assessment, completing and obtaining any required patient health exams, and validating all the information often takes several weeks to months. In this fast-paced world, customers are prone to lose interest in finalizing policies from companies who take a prolonged time to evaluate an application. With the advent of data analytics, the underwriting process can be streamlined and completed much faster	life insurance, insurer, missing value, neural network, proc hpsplit, logistic regression, machine learning, interval variable, feature extraction, response variable, data mining, logistic distribution, regression analysis	6
10	Comparing Dates without an Array	Laurie Smith	This macro will help a beginner or better SAS user compare dates for a subject in separate observations against each other without using a transpose with an array. `PROC SQL` allows a user to compare and retain dates that satisfy a defined condition, creating a final dataset with those desired dates	proc sql, proc step	4
11	Comparison of three methods for transforming predictor variables to improve model fit using SAS	Douglas Thompson	Continuous and ordinal predictor variables are common in predictive modeling (e.g., age in years, medical expenditures last year). Often, such variables are non-linearly related to the predictive modeling target. To maximize the accuracy of a predictive model, non-linear associations need to be taken into account and included in the final model when appropriate. There seems to be no consensus on how best to detect and quantify non-linear associations when building predictive models. Several methods have been proposed in the literature, including cubic splines and exploring a wide va riety of functional forms then selecting the best-fitting via stepwise techniques	regression analysis, interval variable, sample, logistic regression, predictor variable, logistic distribution, proc step, proc adaptivereg, macro definition, health insurance, stepwise selection, data cleaning	13
12	Connecting to Datasets through Python and SAS	Joe Matise	Python is a powerful tool for working with data, and makes the perfect partner to SAS to process data, particularly data that is obtained from online sources where programmers have developed open source Python code to connect to published APIs. SAS has developed two tools to make working with Python easier for SAS programmers, without needing to know nearly any Python - and also made working with SAS easier for Python programmers who do not know SAS! In this paper, we explore these tools, as well as how to use SAS with Jupyter Notebooks, and show how to download some data from a few data sources. This paper is aimed at an intermediate level programmer, but does not require particular SAS or Python knowledge and should be comprehensible to anyone. A basic understanding of using APIs to access data can be helpful but is not required	sas programmer, proc step	9
13	Creating a True LSF Batch Job Submission Capability on SAS EG in a SAS Grid	Derek Grittmann, Adam Hendricks	The Centers for Medicare and Medicaid (CMS) Chronic Conditions Warehouse (CCW) Virtual Data Research Center (VRDC) is a large multi-user SAS Grid environment where most of the users are limited to SAS EG with no command line interface. LSF is a powerful true batch tool that allows a user to launch dozens of SAS jobs and then log out of environment but only from the command line interface. It is possible to make LSF batch available to EG-only users with adequate security using custom macros, script modifications, and SAS invocation options tied to Linux/Unix group membership	sas log, batch job, sas enterprise guide, sas grid	16
14	Deploying SAS Viya Docker images to the cloud - a step by step guide	Alan Zablocki	In this paper, we describe the steps to deploy a pre-built SAS Viya Docker image to the cloud. We use Microsoft Azure as the cloud environment to deploy our single programming-only image. We explain how to push massive Docker images to a private Azure Container Registry (ACR) and how to launch Azure Container Instance (ACI) to host a Docker image. We also show how to share code and data between Azure Storage and the SAS Viya Docker image, providing a complete data science working environment. Finally, we demonstrate how to use our SAS Viya environment for machine learning with code examples using SASPy, SAS Scripting Wrapper for Analytics Transfer (SWAT), TensorFlow and R	sas viya, machine learning, data science, sas studio, neural network, validation set, data cleaning	25
15	Deploying SAS Viya to Docker - a practical guide for data scientists	Alan Zablocki	Recently, SAS has provided the ability to package and deploy SAS Viya software in Docker containers. Users with a valid SAS Viya order have a choice between using the Docker image repository hosted by SAS or manually building the Docker image using SAS container reci pes, an open source GitHub project. In this paper, we use SAS container recipes to build a customized SAS Viya Docker image with access to a range of licensed SAS products such as SAS Visual Statistics and SAS Visual Data Mining and Machine Learning. We guide the reader through the entire process of building and deploying a local SAS Viya Docker image, from configuring local storage and data persistence to adding support for Jupyter Notebook, Python and R	sas viya, sas studio, machine learning	19
16	Detecting Side Effects and Evaluating Effectiveness of Drugs from Customers' Online Reviews using Text Analytics and Data Mining Models	Thu Dinh	Drug reviews play a very important role in providing crucial medical care information for both healthcare professionals and consumers. Customers are increasingly utilizing online review sites, discussion boar ds and forums to voice their opinions and express their sentiments about experienced drugs. However, a potential buyer typically finds it very hard to review all of these online comments before making a purchase decision. Another big challenge would be the unstructured, qualitative, and textual nature of the reviews, which makes it difficult for readers to classify the comments into meaningful insights. The aim of this research is to create a data-mining model to evaluate the effectiveness and detect potential side effects from online customer reviews on specific prescriptive drugs	response variable, data cleaning, proc freq, data mining, feature selection, parsing, proc hpsplit, neural network, probability distribution, regression analysis, validation set, histogram, character variable, training set	23
17	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	20
18	Do undergraduates need prerequisites for common courses?	Lin Qi, Archana Chinnaswamy, Miriam McGaugh		logistic regression, regression analysis, proc hpsplit, logistic distribution, estimate, data mining	6
19	Doing More with the SGPLOT Procedure	Joshua M. Horstman	Once you've mastered the fundamentals of using the `SGPLOT` procedure to generate high-quality graphics, you'll certainly want to delve in to the extensive array of customizations available. This workshop will move beyond the basic techniques covered in the introductory workshop. We'll go through more complex examples such as combining multiple plots, modifying various plot attributes, customizing legends, and adding axis tables	proc sgplot, proc step, group by, scatter plot, box plot, line chart, nominal variable, numeric variable	18
20	DOMinate your ODS Output with PROC TEMPLATE, ODS Cascading Style Sheets (CCS), and the ODS Document Object Model (DOM)	Louise S. Hadden, Troy Martin Hughes	SAS practitioners are frequently forced to produce SAS output in man datory formats, such as using a company logo, corporate or regulated government templates, and/or cascading style sheet (CSS). SAS provides several tools to enable the production of customized output. Among these tools are the Output Delivery System (`ODS`) Document Object Model (DOM), cascading style sheets, `PROC TEMPLATE`, and `ODS` style overrides (usually applied in procedures and/or in originating data). This paper and presentation investigates "under the hood" of the Output Delivery System destinations and the `PROC REPORT` and investigates how mastering `ODS TRACE` DOM and controlling styles with the CSSSTYLE= option, `PROC TEMPLATE`, and style overrides can satisfy client requirements and enhance `ODS` output	proc step, proc report, proc template, output delivery system, ods html, txt, proc format, if-then-else, &variable, format statement, sas output, excel	18
21	Evaluate your SCORE: Logistic regression prediction comparison using the SCORE statement	Robert G. Downer	The SCORE statement in `PROC LOGISTIC` was introduced in `SAS/STAT` 9.0 and it is a feature that can be utilized efficiently to quickly evaluate prediction performance for new observations. Used in conjunction with the OUTMODEL and INMODEL statements, the SCORE statement can be a very beneficial aid in quickly comparing the prediction performance of multiple logistic regression models for the same test or validation observations. The concise syntax of these statements will be illustrated. Performance criterion output such as the misclassification rate will be discussed through a worked example involving multiple models of a binary response. Although some knowledge of logistic regression would be beneficial for full understanding of this paper, it is written for a general audience interested in predictive modeling	proc step, logistic distribution, proc logistic, logistic regression, test set, proc surveyselect, regression analysis, type ii error, proc freq, type i error, training set, sample	6
22	Explore your data before you rush to analysis, you will thank me later: explorations in cross section data	Steven C. Myers	Economists, business leaders and analysts spend a great deal of time analyz ing structured cross sectional data. This paper is an introduction to exploratory data analysis for economic and business data analytic students in an introductory course in economics to teach data handling and SAS programming and features SAS PROCS MEANS, UNIVARIATE, `SGPLOT`, FREQ, TABULATE, CORR, TTEST and REG. A data set on rents paid is used to illustrate the solution of the problem: do female students pay higher rents than male students? It is essential to learn all you can about your data before rushing to analysis, yet analysts typically rush to more advanced and fancier techniques by ignoring or giving only cursory concern to the underlying data. In this paper we show how to ground the analysis in a firm understanding of the data generating process and suggest many ways to learn about the under lying data	proc step, regression analysis, correlation, sample, outlier, standard deviation, proc reg, null hypothesis, proc corr, proc means, histogram, box plot, binary variable, proc freq, proc sgplot, proc ttest, cross-sectional data, proc tabulate, partial correlation, format statement, proc univariate, degrees of freedom, normal distribution, central tendency, data science, response variable, panel data, estimate, sample size	28
23	Exploring and characterizing time series data in a non-regression based approach	Steven C. Myers	Business leaders as well as data analysts and data scientists need to have an understanding of the particularities of time series data. This paper reports on an introduction to time series as taught to students in a first business analytics course making use of data from FRED, the marvelous time s eries repository at the St. Louis Federal Bank. Students are cautioned not to run to advance techniques before stopping to fully explore the data and this approach is designed to instill a EDA mentality into the students while teaching them how to manipulate and characterize time series data in SAS and thereby set the ground work for more advanced work in time-series econometrics, forecasting and predictive analytics. Also, instilled in the students is an appreciation of knowing the data generating process . SAS programming is taught through this approach focusing on SAS functions such as DIF and LAG, PROCS CORR, MEANS, TABULATE and `SGPLOT`	time series analysis, regression analysis, correlation, excel, proc step, time series data, proc freq, stationarity, data transformation, box plot, macro call, unit root, proc autoreg	20
24	Exploring Wine Reviews: How Language and Word Use Varies in Wine Reviews	Abigail Zysk, Kylie Springer	With thousands of varieties of wine, wine descriptions are diverse and unique to the individual describing the wine. A dataset including the wine variety, reviewer, and wine descriptor/flavor words was used to explore the frequency of each word used within certain varieties of wine, for individual reviewers, and for the combination of variety and reviewer. By examining the word usage for different varieties of wine for the top reviewers, we saw that the most used wine descriptors were not exclusive to varieties of wine, but dependent on the wine reviewer. Roger Voss, who had the largest amount of reviews, used the word 'rich' when using a wine descriptor 17.79% of the time. This was reflected across different varieties of wines. When reviewing Bordeaux-Style Red Blends he used the word 'rich' 21.71% of the time when using any wine descriptor, 17.39% for Chardonnay, and 18.14% for Pinot Noir	proc step, proc sql, proc freq, index	8
25	Fifteen Functions to Supercharge Your SAS Code	Joshua M. Horstman	The number of functions included in SAS software has exploded in recent versions, but many of the most amazing and useful functions remain relatively unknown. This paper will discuss such functions and provide examples of their use. Both new and experienced SAS programmers should find something new to add to their toolboxes	anyalpha, if-then-else, character functions, tranwrd function, notalpha, sas programmer, translate, whichn, missing value	8
26	Findings About: De-mystifying the When and How	Michael Wise, Soumya Rajesh	CDISC offers Findings About (FA) and Supplemental Qualifiers (SuppQual) to handle information that doesn't fit into standard domains - or 'Non-standard variables'. They are however, quite distinct from each other and the appropriate use for each may still lead to confusion. "When should FA be created?" or "When is it best to use SUPPQUAL?" These are important questions that can only be answered by asking additional data questions. When the data does not fit into the parent domain, it may only be mapped to SUPPQUAL if it relates to one parent record. However, almost all other situations are covered by FA - wherein data relates to multiple records, or when a two-way relationship is needed etc. FA would be the right approach then, because it has versatility beyond what's offered by SUPPQUAL. For example, FA would provide a way of storing symptoms along with the time that they began and relating each back to the AETERM in the AE dataset	adverse events, sdtmig, cdisc	9
27	Frequency matching case-control techniques: an epidemiological perspective	Hai Nguyen		do loop, proc sql, sample, proc freq, proc step	8
28	From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing	Troy Martin Hughes	With great fanfare, the release of SAS 9 delivered multithreaded processing to a single -threaded SAS world. Procedures such as SORT, SQL, and MEANS could now run faster by taking advantage more fully of system resources through parallel processing paradigms. Multithreading commonly implements divide-and-conquer methodologies in which data sets or data streams are decomposed into subsets and processed in parallel rather than in series . Multithreaded solutions are faster (but typically not more efficient ) than their single-threaded counterparts because execution time (but not system resource utilization) is decreased. As the costs of memory and processing power have continued to decrease, however, there remains no excuse for not implementing multithreaded processing wherever possible. To this end, and because SAS unfortunately abandoned some hapless procedures in single-threaded Sheol, this text aims to reunite the single-threaded `FREQ` procedure with its multithreaded bedfellows	proc freq, parallel processing, sample, macro variable, character variable, proc step, frequency analysis, sas base, repeated measures, macro definition, stress testing, correlation, numeric variable, macro loop, key performance indicator, sysrc, stepwise selection, regression analysis, proc sql, %if-%then-%else	22
29	Generating SAS Datasets from ASCII Files Using a Crosswalk	Jose Centeno	In real-life applications, it is common to have a corresponding crosswalk in order to read raw files into SAS, especially if you're dealing with numerous output files and hundreds of variables with particular formats attached. In many cases, you will find yourself with the task of removing, adding, and/or updating variables which could become challenging or tedious. In this paper we will describe how with the help of a few macros we can reduce this effort as well as greatly decrease the number of lines of c ode in your main program. This program will be easier to maintain, less error-prone and will be easily deployed for other projects. This paper assumes a basic understanding of SAS `DATA STEP` programming, and a basic understanding of SAS Macros	input statement, attrib statement, macro variable, dataset, txt, macro definition, data step, proc sql	8
30	Have Your SAS Program and Schedule It Too!	Mario Tejada	In some SAS environments, it is common to use the Windows Task Scheduler to launch production jobs on a set schedule. In order to create a basic task, one needs to manually open Task Scheduler and enter the desired parameters for the job. This paper will explore the possibility of using SAS to drive the scheduling process. Instead of going into Task Scheduler, the programmer can use SAS to set the parameters of a job and programmatically register that task in Windows using PowerShell. This paper assumes a basic understanding of SAS `DATA STEP` programming, SAS Macros and administrator-level access to run Windows Operating system (PowerShell) commands. Some familiarity with creating scheduled tasks is assumed. Programs on this paper were tested using SAS 9.4M5 and PowerShell version 5.1 on Windows 10	data step, filename statement	4
31	Impact of Aging Population on Social Security and Underlying Trends	Prajakta Pai, Miriam McGaugh	For years Social Security has been a major source of income for retired individuals and their families. Social Security was developed as an anti-poverty program that focuses on retired workers, disabled individuals and survivors of workers. Over the years, factors such as improved longevity, educati on and retirement of baby boomers has resulted in strain on the Social Security reserves. Research suggests the Social Security benefits will soon be reducing due to more beneficiaries within the system. Currently, over 61 million beneficiaries are paid each month. However, with the retirement of the baby boomer population, the Social Security funds will see an increase in payouts and a decrease of income. It is imperative to look at the trends in population versus individuals who would be availing Social S ecurity upon retirement to establish a trend and identify factors that may contribute towards a deficit or surplus in Social Security funds for the coming years	variable list, proc step	8
32	Interactive Graphs	Richann J. Watson, Kriss Harris	This paper demonstrates how you can use interactive graphics in SAS 9.4 to assess and report your safety data. The interactive visualizations that you will be shown include the adverse event and laboratory results. In addition, you will be shown how to display "details-on-demand" when you hover over a point. Adding interactivity to your graphs will bring your data to life and help improve lives!	change from baseline, bar chart, series plot, box plot, adverse events, sample, macro variable, scatter plot, macro loop, by group processing	24
33	Legislative Action on Human Trafficking: Towards a Data-Driven Policy	David J. Corliss	Previous meta-analysis of state-level human trafficking data reported by Polaris identified demographic, economic, and sociological drivers of human trafficking. This enabled prediction of the number of reported victims to be expected for each state. States with high-performing programs and practices for identifying victims have a higher reported rate than that predicted by demographic and socio-economic drivers alone. This new meta-analysis compares the legislative environment of higher performing states to those with fewer reported cases for the same expected underlying rate. In this way, insight is gained as to wh ich legislative actions are more effective in identifying victims. For example, training of emergency room workers is an effective practice, yet as many as 37% states have no such requirement. This application provides data-driven recommendations for which legislative actions have the largest effect in driving the reporting of human trafficking victims	proc mixed, fixed effects, proc step, proc glimmix, data science	9
34	Leveraging RAREEVENTS Procedure Options to Monitor and Evaluate Infrequent Events in Healthcare	Lynn (Xiaohong) Liu, Roderick Jones	In healthcare, the purpose of statistical process control (SPC) is often to quantify improvements and identify unintended consequences resul ting from an intentional change in an environment, policy, treatment protocol, or decision-support tool. Appropriate for measurement of infrequent or low-probability events, the `RAREEVENTS` procedure is valuable for healthcare quality monitoring and evaluation. The `RAREEVENTS` procedure was enhanced in the `SAS/QC` version 15.1 release and now allows users to apply tests to detect special cause variation. We provide an example of leveraging new `PROC RAREEVENTS` options, including READPHASE =, PHASELEGEND, LIMITPHASES, TEST1, TEST2, TEST3 , TEST4 and	proc step, adverse events	10
35	Like, Learn to Love SAS Like	Louise S. Hadden	How do I like SAS? Let me count the ways.... There are numerous instances where LIKE or LIKE statements can be used in SAS - and all of them are useful. This paper will walk through such uses of LIKE as: searches and joins with that smooth LIKE operator (and the NOT LIKE operator); the SOUNDS LIKE operator; using the LIKE condition to perform pattern-matching and create variables in `PROC SQL`; and `PROC SQL` CREATE TABLE LIKE	proc sql, proc step, create table, where statement	7
36	Logistic Regression, Basics and Beyond	Bruce S. Lund	This paper presents light theory, supported by simulations, as well as practical suggestions for developing binary logistic regression models. Topics include: Firth method versus maximum likelihood method for estimating parameters; screening, binning, transforming predictors; identification of multicollinearity; predictor selection methods using `PROC LOGISTIC`, `PROC HPGENSELECT`; and measures of fit and predictive accuracy	logistic distribution, sample, logistic regression, proc step, proc logistic, binary variable, correlation, sample size, regression analysis, proc hplogistic, data cleaning, likelihood function, standard deviation, maximum likelihood estimation, confidence interval, proc calis, degrees of freedom, type i error, proc reg, validation set, proc hpsplit, cumulative frequency, probability distribution, estimate, proc hpgenselect, training set	35
37	Look Up Not Down: Advanced Table Lookups in Base SAS	Jayanth Iyengar, Joshua M. Horstman	One of the most common data manipulation tasks SAS programmers perform is combining tables through table lookups. In the SAS programmer's toolkit many constructs are available for performing table lookups. Traditional methods for performing table lookups include conditional logic, match-merging and SQL joins. In this paper we concentrate on advanced table lookup methods such as formats, multiple `SET` statements, and HASH objects. We conceptually examine what advantages they provide the SAS programmer over basic methods. We also discuss and assess performance and efficiency considerations through practical examples	hash table, lookup, set statement, cpu time, proc step, dataset, conditional do loop, proc format, proc sql, data merging, do loop, put statement, key, insurance claims, sas log, key=, sas programmer, merge statement, sas base, temporary array, output, sample, sysrc, sample size, index, stop, user-defined format, call missing, if-then-else	24
38	Machine Learning? The Machine Already Knows. Software-Intelligent Application Development Provides Reliability, Reusability, Extendability, and Maintainability for Strong Smart Systems in an Ever-Changing World	LeRoy Bessler	Applications designed and built with Software Intelligence (SI) are robust, made of reusable parts, and easy and quick to extend or maintain. With dynamically auto-customizing code, such "living" applications go beyond change tolerance to change amenability, and further-to change implementation. They cope with ever-changing user or management preferences, run-dates, data dates, and data content. Common types of changes in report/graph content, format, and function are handled without reprogramming. If business rules do not change, an SI application can have eternal life. Whether you are a new or experienced SAS programmer, or an analytically oriented user who does not think of herself or himself as a programmer at all, this paper-which assumes no advanced SAS knowledge-shows you how to apply principles of Software-Intelligent Application Development, which are really programming-language-independent, to make your use of SAS software safe, simple, and speedy	macro facility, macro definition, sas log, symbol table, macro variable	18
39	Navigating the Ins and Outs of Mapping in SAS Enterprise Guide	Ruth Kurtycz	Mapping is an extremely valuable data visualization tool. Maps are approachable and intuitive, which can be especially useful when trying to display data in such a way that will reach an audience without an analytical background. Although SAS has the capabilities to easily produce maps at the global, country, and state level, creating maps for smaller geographic areas requires some extra steps. This paper will explain how to create zip code and census tract level maps using SAS Enterprise Guide. We will go over the basics of obtaining zip code and census tract shapefiles and importing them with the `MAPIMPORT` procedure. We will explain how to annotate a map using the %ANNOMAC and %MAPLABEL macros, and the process of creating the final visualizations with the GPROJECT, GREMOVE, and GMAP procedures. We will also touch on prepping data for mapping and creating formats for the data categories. The example maps display infant mortality data from 2001 to 2018 for Kent County, Michigan	proc step, sas enterprise guide, proc gmap, proc sql	10
40	No Wrong Door	Jack N. Shoemaker	This is a case study of new implementation of SAS on a Linux platform and the migration and transition of the SAS ecosystem to an open and flexible topology anchored by the new platform	sas enterprise guide, macro variable, data science, options statement, proc http, rest api, proc step	8
41	Not Even One Single Solitary Semicolon: Powerful SAS Things You Can Do Without Writing Programs	Ben Cochran	This presentation starts by illust rating the conversion of different kinds of data into SAS data sets. Specifically, `EXCEL` spreadsheets and Microsoft Access t ables are converted into SAS data. Then, these two different data sources are joined with an existing SAS data set. Finally, a series of graphical and tabular reports are generated from this comb ined data. All of these tasks are completed without writing any SAS programs	dataset, bar chart, excel, sas enterprise guide	16
42	Oh, There's No Place Like SAS ODS Graphics for the Holidays!	Ted Conway	Already a SAS `ODS` Graphics user at work, the author used the (free!) SAS University Edition software he'd recently downloaded and installed on his home laptop to knock out a connect -the-dots Tom Turkey with `PROC SGPLOT` to commemorate Thanksgiving 2015. And so began an ongoing series of "Fun with SAS `ODS` Graphics" posts on the SAS Support Communities and Twitter that celebrated major holidays and other events. While creating these admittedly frivolous charts from the comfort of his easy chair, the author learned some useful techniques for creating serious data vizzes, which will be shared in this e- poster and the accompanying paper	proc step, bubble plot	27
43	Parallel Processing Your Way to Faster Software and a Big Fat Bonus: Demonstrations in Base SAS	Troy Martin Hughes	SAS software and especially e xtract-transform-load (ETL) systems commonly include components that must be serialized due to real process dependencies. For example, a transform module often cannot begin until the data extraction completes, and a corresponding load module cannot begin until the data transformation completes. Although process dependencies such as these cannot be avoided in many cases and necessitate serialized software design, in other cases, programs or data can be distributed across two or more SAS sessions to be processed in parallel , facilitating significantly faster software . This text introduces the concept of false dependencies, in which software is serialized by design rather than necessity , thus needlessly increasing execution time and deprecating performance	macro variable, parallel processing, proc freq, proc corr	8
44	Powerful and "Sometimes" Hard-to-find PROC SQL Features	Kirk Paul Lafler	The `SQL` procedure is a wonderful tool for querying, subsetting, ordering, and grouping/regrouping data; creating tables; conducting logic scenarios with the CASE expression; performing table lookup, matching and join operations; constructing and accessing virtual tables (views); creating subqueries for special processing needs; and other operations. Occasionally, problems come along where SQL is either better suited or easier to code than conventional DATA and/or `PROC STEP` methods. This hands- on workshop (HOW) highlights `PROC SQL` SELECT queries every SAS user should know to harness the power of this powerful data base language	proc sql, proc step, where statement, inner join, macro variable, select, macro facility, view, sas log, dictionary tables, macro call, order by, right join, sas library	14
45	Powerful SAS Output Delivery with ODS EXCEL	LeRoy Bessler	A common destination for results prepared with SAS is often an `EXCEL` workbook. Everyone already has `EXCEL` and knows how to use it, to reformat or further explore their results however they wish. `ODS` `EXCEL` enables a SAS programmer to create highly formatted reports, tabular or graphic, or a combination of both, that can be opened and used with `EXCEL`. You can turn on customization/formatting features in SAS that would be possible manually inside `EXCEL`, to deliver an already finished product to the viewer of the report. The `ODS` `EXCEL` capability does not require `EXCEL` to be installed on the machine that creates `ODS` `EXCEL` output. You can use `ODS` `EXCEL` running SAS on MVS, UNIX, Linux, or Windows. This paper assumes no prior knowledge of the topic. `ODS` `EXCEL` output requires Microsoft `EXCEL` 2010 or later	excel, macro variable, macro definition	78
46	Profiling hospital length of stay using the mode	Anne Cain-Nielsen, Scott Regenbogen		central tendency, outlier	1
47	Quick, Call the "FUZZ": Using Fuzzy Logic	Richann J. Watson, Louise S. Hadden	SAS practitioners are frequently called upon to do a comparison of data between two different data sets and find that the values in synonymous fields do not line up exactly. A second quandary occurs when there is one data source to search for particular values, but those values are contained in character fields in which the values can be represented in myriad different ways. This paper discusses robust, if not warm and fuzzy, techniques for comparing data between, and selecting data in, SAS data sets in not so ideal conditions	proc step, index, where statement, decimal, fuzz, if-then-else, prxmatch, proc fcmp, upcase function, sample, character variable	30
48	Sample Size and Design Considerations in Studies Assessing Non-Inferiority using Continuous Outcomes	Michael G. Wilson	Software developers employ incremental progress to cause radical development break throughs. The same is true in medicine, manufacturing and finance. For example, a new anti-diabetic medicine might not have superior outcomes of improved glycemic control, but it might be less expensive. Or a new device for use in hand surgery might not have superior digital mobility, but might be easier for the surgeon to implant. Or perhaps micro-loans to novice entrepreneurs might not raise the economic output for the county, but might cultivate cooperation among local businesses. These are examples where the outcome of a new method might not be objectively worse, that is non-inferior, but would have some reason to replace the current method and instigate incremental progress. SAS users are often asked to size and design studies to test this kind of non -inferiority	alternative hypothesis, null hypothesis, sample, sample size, hypothesis testing, type ii error, missing value, type i error, confidence interval, standard deviation, proc power, normal distribution, binomial distribution, clinical trial	19
49	SAS Macro Programming Tips and Techniques	Kirk Paul Lafler	The SAS Macro Language is a powerful feature for extending the capabilities of the SAS System. This paper highlights a collection of techniques for constructing reusable and effective macros tools. Attendees are introduced to the techniques associated with building functional macros that process statements containing SAS code; design reusable macro techniques; create macros containing keyword and positional parameters; utilize defensive programming tactics and techniques; build a library of macro utilities; interface the macro language with the `SQL` procedure; and develop efficient and portable macro language code	macro variable, excel, macro facility, proc step, proc sql, autocall macro, macro definition, macro function, macro parameter, %macro parameters, dictionary tables, ods excel, sas log, output delivery system, options statement, macro loop, &variable, proc freq	13
50	Saving and Restoring Startup (Initialized) SAS System Options	Kirk Paul Lafler	Processing requirements sometimes require the saving (and restoration) of SAS System options at strategic points during a program's execution cycle. This paper and presentation illustrates the process of using the OPTIONS, OPTSAVE, and OPTLOAD procedures to perform the following operations: Display portable and host-specific SAS System options and their settings; Display restricted SAS System options; Display SAS System options that can be restricted; Display information about SAS System option groups; Display a list of SAS System options that belong to a specific group; Display a list of SAS System options that can be saved; Save startup SAS System options; Restore startup SAS System options, when needed	options statement, sas log, proc options, dataset	6
51	Scatterplots: Basics, Enhancements, Problems, and Solutions	Peter L. Flom	The scatter plot is a basic tool for presenting information on two continuous variables. While the basic plot is good in many situations, enhancements can increase its utility. I also go over tools to deal with the problem of overplotting	scatter plot, regression analysis, box plot	13
52	Simulating Skewed Multivariate Distributions Using SAS: Cases of Lomax, Mardia's Pareto (Type I), Logistic, Burr and F Distributions	Zhixin Lun, Ravindra Khattree	By using various build-in functions in SAS software, it is easy to generate data from several common multivariate distributions such as multivariate normal (RANDNORMAL function) and multivariate Student's 𝒕 (RANDMVT function). However, functions for generating data from other less common multivariate distributions are not readily available in SAS. We will illustrate the simulation and generation of random numbers from a multivariate Lomax distribution. Importance of the work lies in its wide applicability in reliability theory and many other situations where one needs to use a flexible nonnegative skewed multivariate distribution for modeling. Further, based on various useful properties of multivariate Lomax distribution, Mardia's multivariate Pareto of type I, multivariate Logistic, multivariate Burr, and multivariate 𝑭 random variables can also be readily simulated	sample, proc means, random number generation, logistic distribution, sample size, gamma distribution, degrees of freedom, histogram	11
53	Survival Tips for Survival Analysis	Xiaoting Wu	Survival analysis is a common type of analysis in health care field. This hand-on tour will convey some survival tips during survival analysis. We will provide an overview on the survival analysis using SAS LIFETEST and PHREG procedures, including data preparation and visualization, variable selection, model specification, model validation and output interpretation. We will also showcase some advanced application on how to obtain prediction estimates, customize and output plots from SAS `PHREG` procedure	survival analysis, proc phreg, response variable, hazard ratio, hazard function, feature selection, interval variable, likelihood function	13
54	Surviving Survival Analysis 101: Making the Likelihood Ratio Test Easier Using a Macro	Katelyn Ware, Rachel Baxter	When modeling survival analysis data, if a certain covariate does not meet the proportional hazards assumption, it can still be included in the Cox Proportional Hazards model with stratification. However, one must decide if the slopes of the other covariates are statistically different across strata or not. If they are, an interaction term must be included in the model. The likelihood ratio test is a useful tool when deciding if interaction terms are needed in a stratified Cox Proportional Hazards model. The -2logL values may be obtained with `PROC PHREG` and used to compute a chi-square likelihood ratio test and p-value. The following paper describes a MACRO created in SAS 9.4, which automates this test	likelihood function, proc phreg, proportional hazards assumption, survival analysis, hazard function, null hypothesis, interaction effect, regression analysis, main effect, proc step, predictor variable, maximum likelihood estimation, estimate, binary variable, proc reg, macro variable	13
55	Taxi Ride Prediction: Does the Yellow Cab Supply Meet Customer Demands?	Sreejita Biswas, Miriam McGaugh	New York is the taxi capital of America and home to the classic yellow taxicabs. It wou ld be beneficial to taxi companies and customers if rides are available whenever a customer is in need of one. To achieve this level of service, it is important to know how different factors influence the number of rides. This process gets complicated due to the effects of ex ternal forces such as weather and d ue to pricing strategies employed by other cab companies such as surge pricing during heavy demands times. This paper attempts to predict the demand of a yellow taxi at a particular location, on a particular day and at a particular time. This will help to estimate the number of taxis that should be present at any given time or place. This project focuses on NY Yellow Taxis dispatched from a central facility in 2018. This paper will help to understand and predict the demand and supply of yellow taxicabs and help t o improve customer satisfaction and the taxi cab industry's efficiency	time series analysis, data mining	12
56	The Advent of Renewable Energy	Sai Teja Sagi, Miriam McGaugh	Renewable energy accounted for 12.2 % of total primary energy consumption and 14.94 % of the domestically produced electricity in the United States in 2016[2]. The development of renewable energy and energy efficiency marked "a new era of energy exploration" in the United States, according to former President Barack Obama[2]. This descriptive research focuses on how the usage of renewable and non - renewable energy has changed over the past 27 years in the USA. First, this research identifies correlations between renewable and non-renewable energy usage patterns. Second, this research explores the impact of economic factors on energy usage. Having a clear picture of energy usage by the world nations can help make effective policies for renewable energy adoption by different world countries. The data set is provided by the UN and it is used in SAS Enterprise GuideTM. Statistical methods such as time series analysis, trend line studies are used to make the visualizations meaningful	correlation, group by, create table, order by, pearson correlation, proc sql, time series analysis	21
57	The Doctor Ordered a Prescription ... Not a Description: Driving Dynamic Data Governance Through Prescriptive Data Dictionaries That Automate Quality Control and Exception Reporting	Troy Martin Hughes	Data quality is a critical component of data governance and describes the accuracy, validity, completeness , and consistency of data. Data accuracy can be difficult to assess, as it requires a comparison of data to the real -world constructs being abstracted. But other characteristics of data quality can be readily assessed when provided a clear expectation of data elements, records, fields, tables, and their respective relationships. Data dictionaries represent a common method to enumerate these expectations and help answer the question What should my data look like? Too often, however, data dictionaries are conceptualized as static artifacts that only describe data. This text introduces dynamic data dictionaries that instead prescribe business rules against which SAS d ata sets are automatically assessed, and from which dynamic, data-driven, color-coded exception reports are automatically generated	data dictionary, data validation rules, data cleaning, variable, excel, quality control, if-then-else, xls, dataset, macro definition, proc import, proc format, proc step, macro variable	20
58	The SAS EG Process Flow: A Customizable Data Mining Tool in the Search for Healthcare Fraud	Andrea McLain	It is estimated that tens of billions of dollars are lost each year in fraudulent healthcare insurance claims. The implications of this go way beyond financial losses and higher insurance premiums. For instance, many fraud schemes could result in patient exploitation or harm, or the illicit gains could be used in the furtherance of other criminal activities. Health insurance companies utilize data mining and predictive analytics to identify potentially fraudulent claims. Many third party companies create products just for this very purpose, where algorithms are used to flag claims exhibiting some known fraudulent pattern. Products built by these companies are exceptionally helpful in identifying and ultimately stopping and preventing insurance fraud, but many situations call for more, and a means beyond pre -built algorithms are necessary	sas enterprise guide, insurance claims, health insurance, data mining, inner join	6
59	Time-To-Event Analysis in the Presence of Competing risks	Xiaoting Wu	Competing risks are common phenomena in time-to-event analysis. A competing risk may take place before the event of interest thus exclude the possibility of event occurrence. For example, in the study of artificial heart valve duration, death is a competing risk as it modifies a patient's chance to receive potential reoperation due to valve deterioration. Ignoring competing risks, for example, the use of standard Kaplan-Meier estimators, will result in biased estimates for the event of interest. Cumulative incidence function that estimates the probability of event of interest over time, and cause- specific hazard function that models the effect of covariates on the event of interest, are two main approaches to perform time-to-event analysis in the presence of competing risk. This paper demonstrates the rational, implementation and interpretation of these methods, with SAS applications using SAS macro % CIF , LIFETEST and `PHREG` procedure	competing risks, cause-specific hazard, hazard function, survival analysis, proc phreg	9
60	Timing is Everything: Defining ADaM Period, Subperiod and Phase	Nancy Brucken	The CDISC Analysis Data Model Implementation Guide (ADaMIG) provides several timing variables for modeling clinical trial designs in analysis datasets. APHASE, APERIOD and ASPER can be used in conjunction with related treatment variables to meet a variety of analysis requirements, from single-period parallel studies to much more complicated situations involving multiple treatment periods and even different studies. The goal of this paper is to illustrate how some of these study designs may be handled in ADaM, and provide guidelines for selecting when to use the different timing variables that are available	treatment period, adam dataset, cdisc	7
61	Track SAS Viya user logins to identify usage and drive improvement using actionable Intelligence: navigate visual reports, track KPIs and visualize long term trends.	Anurag Kulkarni	Do you want to track how your users are taking advantage of tools within SAS Viya? Do you offer reports to your clients and want to analyze how the reports are being used to drive improvement? Are you a SAS superuser, want to track usage, schedule jobs intelligently and manage your environment proactively? The research will walk you through how to archive the data, derive KPIs and build interactive visuals to track this information	sas viya, audit trail, sas studio	5
62	Understanding Administrative Healthcare Datasets using SAS programming tools	Jayanth Iyengar	Changes in the healthcare industry have highlighted the importance of healthcare data. The volume of healthcare data collected by healthcare institutions, such as providers and insurance companies is massive and growing exponentially. SAS programmers need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. There are various types and sources of administrative healthcare data, which include Healthcare Claims (Medicare, Commercial Insurance, & Pharmacy), Hospital Inpatient, and Hospital Outpatient. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. The workshop will engage attendees with a series of SAS exercises involving healthcare datasets	proc step, proc freq, sas programmer, proc sort, proc tabulate, insurer, health insurance, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc contents, proc report, sas programming, by group processing, dataset, proc means, lookup	24
63	Urge to MERGE? Maybe You Should UPDATE Instead	Ben Cochran	The `DATA STEP`'s UPDATE statement is similar to the MERGE, but it has so me helpful built-in logic of which many users of SAS may not be familiar. In most case s, this built in logic can yield much simpler DATA steps. This paper sheds light on so me of these build-in features and takes a step-by-step approach to showing you how to take advantage of this power that is already there	update statement, merge statement, missing value, dataset	8
64	US Airline Passenger Satisfaction using SAS Enterprise Miner	Harish Reddy Patlolla, Miriam McGaugh	In the past 20 years, the aviation industry has been growing rapidly. This growth of the industry provides opportunities as well as challenges. While the opportunities arise because of increasing demand, rival airlines pose threat. Apart from optimizing pricing, have you ever wondered what airlines do to overcome these threats? Passenger Satisfaction. Unhappy passengers mean fewer customers and less revenue. Therefore, it is important that passengers have a rich experience every time they travel . The satisfaction survey from 259,760 passengers, which is a combination of categorical and continuous variable has been used for this study. The models have been built using decision trees and logistic regression. This study seeks to not only explain the paramount factors which impact the passenger satisfaction in the US Airline industry but also change in those factors across different age groups	proc hpsplit, data cleaning	12
65	User-Defined Multithreading with the SAS DS2 Procedure: Performance Testing DS2 Against Functionally Equivalent DATA Steps	Troy Martin Hughes	The `DATA STEP` 2 (DS2) procedure affords the first opportunity for developers to build custom, multithreaded processes in Base SAS. Multithreaded processing debuted in SAS 9, when built -in procedures such as SORT, SQL, and MEANS were threaded to reduce runtime. Despite this advancement, and in contrast with languages such as Java and Python, SAS 9 still did not provide developers the ability to create custom, multithreaded processes. This limitation was overcome in SAS 9.4 with the introduction of the `DS2` procedure-a threaded, object -oriented ve rsion of the `DATA STEP`. However, because DS2 relies on methods and packages (neither of which have been previously available in Base SAS), both DS2 instruction and literature has predominantly fixated on these object -oriented programming (OOP) aspects of t he language rather than DS2 multithreading	cpu time, if-then-else, proc freq, automatic variable	25
66	Using Macro Variable Lists to Create Dynamic Data-Driven Programs	Joshua M. Horstman	The SAS Macro Facility is an amazing tool for creating dynamic, flexible, reusable programs that can automatically adapt to change. In this paper, you'll see how macro variable lists provide a simple but powerful mechanism for creating data-driven programming logic. Don't hard-code data values into your programs. Eliminate data dependencies forever and let the macro facility write your SAS code for you!	macro variable, variable list, proc sql, macro facility, proc step, call symputx, macro definition, &variable, macro function, macro loop, macro variable list	10
67	Using ODS Trace (DOM), Procedural Output and ODS Output Objects to Create the Output of Your Dreams	Louise S. Hadden	SAS procedures can convey an enormous amount of information - sometimes more information than is needed. Most SAS procedures generate `ODS` objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and `ODS` templates using `ODS TRACE` (DOM) and by manipulating procedural output and `ODS` OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively	proc step, proc contents, estimate, ods output, output, proc means, excel, sample, data step, proc surveyfreq, logistic distribution, output delivery system	26
68	Using the XLSX libref engine with metadata available in Dictionary Tables	Michael Harper	With the XLSX `LIBNAME` engine, individual worksheet tabs become logical tables you can reference in SAS	macro put, excel	3
69	Utilizing Macros to Create Patient Site Matching via Zip-Code Radiuses	Kathryn Schurr	Determining how to match patients to clinical trial sites is a messy problem. This problem includes many different facets not limited to: inclusion criteria, exclusion criteria, and proximity to clinical trial sites. By creating a site matching program in SAS, users are able to take patient lists and can easily determine whether or not a patient falls within a certain proximity of the site(s) of interest. This macro takes into account the ZIP Code location of the proposed site and the ZIP Code location of the patient. It then is able to compute whether or not that patient should be assigned to any particular site. This paper builds upon an already existing macro that calculates the ZIP Codes within a certain mile radius of a singular site location	macro variable, clinical trial	15
70	What Not to Do in a Program Used with %include	Stephanie R. Thompson	This Rapid Fire session will provide a fast look at what can make a program you list on a `%INCLUDE` statement not work. Based on a frustrating real-world scenario	run, quit	2
71	Why Admitted Students Opt-out of College Enrollment?	Manasi Murde, Miriam McGaugh	Understanding and improving student enrollment has always been important for departments across universities. It is imperative for universities to have a firm grip on addressing enrollment of prospects, applicants and admitted students. As per research, one out of every three students who are admitted do not enroll in that college. One of the prime reasons why universities must concentrate on increasing their enrollment rate is to avoid loss of revenue. Admitted but non-enrolled students have a negative financial impact on a university due to the loss of tuition fees while expending money on marketing and recruitment. Non-enrollment of students also results in loss of academic potential. There can be numerous reasons why admitted students fail to enroll within a particular university, which may be specific to one university or common among many schools. Therefore, each school must identify any existing trends within its own unique admitted non-enrolled student population base	data science, proc means	13

Back to Conf go 2024 go 2018

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.