For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Student growth percentile (SGP) is one of the most widely used score metrics for measuring a student's academic growth. Using longitudinal data, SGP describes a student's growth as the relative standing among students who had a similar level of academic achievement in previous years. Although several models for SGP estimation have been introduced, and some models have been implemented with R, no studies have yet described using SAS^®. As a result, this research describes various types of SGP models and demonstrates how practitioners can use SAS procedures to fit these models. Specifically, this study covers three types of statistical models for SGP: 1) quantile regression-based model 2) conditional cumulative density function-based model 3) multidimensional item response theory-based model. Each of the three models partly uses procedures in SAS, such as PROC QUANTREG, PROC LOGISTIC, PROC TRANSREG, PROC IRT, or PROC MCMC, for its computation. The program code is illustrated using a simulated longitudinal data set over two consecutive years, which is generated by SAS/IML^®. In addition, the interpretation of the estimation results and the advantages and disadvantages of implementing these three approaches in SAS are discussed.

Given the proposed budget cuts to higher education in the state of Kentucky, public universities will likely be awarded financial appropriations based on several performance metrics. The purpose of this project was to conceptualize, design, and implement predictive models that addressed two of the state's metrics: six-year graduation rate and fall-to-fall persistence for freshmen. The Western Kentucky University (WKU) Office of Institutional Research analyzed five years' worth of data on first-time, full-time bachelor's degree seeking students. Two predictive models evaluated and scored current students on their likelihood to stay enrolled and their chances of graduating on time. Following an ensemble of machine-learning assessments, the scored data were imported into SAS^® Visual Analytics, where interactive reports allowed users to easily identify which students were at a high risk for attrition or at risk of not graduating on time.

Higher education institutions have a plethora of analytical needs. However, the irregular and inconsistent practices in connecting those needs with appropriate analytical delivery systems have resulted in a patchwork this patchwork sometimes overlaps unnecessarily and sometimes exposes unaddressed gaps. The purpose of this paper is to examine a framework of components for addressing institutional analytical needs, while leveraging existing institutional strengths to maximize analytical goal attainment most effectively and efficiently. The core of this paper is a focused review of components for attaining greater analytical strength and goal attainment in the institution.

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

Prince Niccolo Machiavelli said things on the order of, The promise given was a necessity of the past: the word broken is a necessity of the present. His utilitarian philosophy can be summed up by the phrase, The ends justify the means. As a personality trait, Machiavelianism is characterized by the drive to pursue one's own goals at the cost of others. In 1970, Richard Christie and Florence L. Geis created the MACH-IV test to assign a MACH score to an individual, using 20 Likert-scaled questions. The purpose of this study was to build a regression model that can be used to predict the MACH score of an individual using fewer factors. Such a model could be useful in screening processes where personality is considered, such as in job screening, offender profiling, or online dating. The research was conducted on a data set from an online personality test similar to the MACH-IV test. It was hypothesized that a statistically significant model exists that can predict an average MACH score for individuals with similar factors. This hypothesis was accepted.

The TABULATE procedure has long been a central workhorse of our organization's reporting processes, given that it offers a uniquely concise syntax for obtaining descriptive statistics on deeply grouped and nested categories within a data set. Given the diverse output capabilities of SAS^®, it often then suffices to simply ship the procedure's completed output elsewhere via the Output Delivery System (ODS). Yet there remain cases in which we want to not only obtain a formatted result, but also to acquire the full nesting tree and logic by which the computations were made. In these cases, we want to treat the details of the Tabulate statements as data, not merely as presentation. I demonstrate how we have solved this problem by parsing our Tabulate statements into a nested tree structure in JSON that can be transferred and easily queried for deep values elsewhere beyond the SAS program. Along the way, this provides an excellent opportunity to walk through the nesting logic of the procedure's statements and explain how to think about the axes, groupings, and set computations that make it tick. The source code for our syntax parser are also available on GitHub for further use.

Graphics are an excellent way to display results from multiple statistical analyses and get a visual message across to the correct audience. Scientific journals often have very precise requirements for graphs that are submitted with manuscripts. While authors often find themselves using tools other than SAS^® to create these graphs, the combination of the SGPLOT procedure and the Output Delivery System enables authors to create what they need in the same place as they conducted their analysis. This presentation focuses on two methods for creating a publication quality graphic in SAS^® 9.4 and provides solutions for some issues encountered when doing so.

The announcement of SAS Institute's free SAS^® University Edition is an exciting development for SAS users and learners around the world! The software bundle includes Base SAS^®, SAS/STAT^® software, SAS/IML^® software, SAS^® Studio (user interface), and SAS/ACCESS^® for Windows, with all the popular features found in the licensed SAS versions. This is an incredible opportunity for users, statisticians, data analysts, scientists, programmers, students, and academics everywhere to use (and learn) for career opportunities and advancement. Capabilities include data manipulation, data management, comprehensive programming language, powerful analytics, high-quality graphics, world-renowned statistical analysis capabilities, and many other exciting features. This paper illustrates a variety of powerful features found in the SAS University Edition. Attendees will be shown a number of tips and techniques on how to use the SAS^® Studio user interface, and they will see demonstrations of powerful data management and programming features found in this exciting software bundle.

Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effect estimates. Cause-and-effect inferences are particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator (MacKinnon, 2008). The focus of this paper is how incorporating longitudinal measures of the mediating and outcome variables aides in the causal interpretation of mediated effects. This paper provides useful SAS^® tools for designing adequately powered studies to detect the mediated effect. Three SAS macros were developed using the powerful but easy-to-use REG, CALIS, and SURVEYSELECT procedures to do the following: (1) implement popular statistical models for estimating the mediated effect in the pretest-posttest control group design; (2) conduct a prospective power analysis for determining the required sample size for detecting the mediated effect; and (3) conduct a retrospective power analysis for studies that have already been conducted and a required sample to detect an observed effect is desired. We demonstrate the use of these three macros with an example.

At the University of Central Florida (UCF), Student Development and Enrollment Services (SDES) combined efforts with Institutional Knowledge Management (IKM), which is the official source of data at UCF, to venture in a partnership to bring to life an electronic version of the SDES Dashboard at UCF. Previously, SDES invested over two months in a manual process to create a booklet with graphs and data that was not vetted by IKM; upon review, IKM detected many data errors plus inconsistencies in the figures that had been manually collected by multiple staff members over the years. The objective was to redesign this booklet using SAS^® Web Report Studio. The result was a collection of five major reports. IKM reports use SAS^® Business Intelligence (BI) tools to surface the official UCF data, which is provided to the State of Florida. Now it just takes less than an hour to refresh these reports for the next academic year cycle. Challenges in the design, implementation, usage, and performance are presented.

Every visualization tells a story. The effectiveness of showing data through visualization becomes clear as these visualizations will tell stories about differences in US mortality using the National Longitudinal Mortality Study (NLMS) data, using the Public-Use Microdata Samples (PUMS) of 1.2 million cases and 122 thousand records of mortality. SAS^® Visual Analytics is a versatile and flexible tool that easily displays the simple effects of differences in mortality rates between age groups, genders, races, places of birth (native or foreign), education and income levels, and so on. Sophisticated analyses including logistical regression (with interactions), decision trees, and neural networks that are displayed in a clear, concise manner help describe more interesting relationships among variables that influence mortality. Some of the most compelling examples are: Males who live alone have a higher mortality rate than females. White men have higher rates of suicide than black men.

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The advent of robust and thorough data collection has resulted in the term big data. With Census data becoming richer, more nationally representative, and voluminous, we need methodologies that are designed to handle the manifold survey designs that Census data sets implement. The relatively nascent PROC SURVEYLOGISTIC, an experimental procedure in SAS^®9 and fully supported in SAS 9.1, addresses some of these methodologies, including clusters, strata, and replicate weights. PROC SURVEYLOGISTIC handles data that is not a straightforward random sample. Using Census data sets, this paper provides examples highlighting the appropriate use of survey weights to calculate various estimates, as well as the calculation and interpretation of odds ratios between categorical variable interactions when predicting a binary outcome.

Hash tables are powerful tools when building an electronic code book, which often requires a lot of match-merging between the SAS^® data sets. In projects that span multiple years (e.g., longitudinal studies), there are usually thousands of new variables introduced at the end of every year or at the end of each phase of the project. These variables usually have the same stem or core as the previous year's variables. However, they differ only in a digit or two that usually signifies the year number of the project. So, every year, there is this extensive task of comparing thousands of new variables to older variables for the sake of carrying forward key database elements corresponding to the previously defined variables. These elements can include the length of the variable, data type, format, discrete or continuous flag, and so on. In our SAS program, hash objects are efficiently used to cut down not only time, but also the number of DATA and PROC steps used to accomplish the task. Clean and lean code is much easier to understand. A macro is used to create the data set containing new and older variables. For a specific new variable, the FIND method in hash objects is used in a loop to find the match to the most recent older variable. What was taking around a dozen PROC SQL steps is now a single DATA step using hash tables.