Data analysis begins with cleaning up data, calculating descriptive statistics, and examining variable distributions. Before more rigorous statistical analysis begins, many statisticians perform basic inferential statistical tests such as chi-square and t tests to assess unadjusted associations. These tests help guide the direction of the more rigorous statistical analysis. How to perform chi-square and t tests is presented. We explain how to interpret the output and where to look for the association or difference based on the hypothesis being tested. We propose the next steps for further analysis using example data.

In Bayesian statistics, Markov chain Monte Carlo (MCMC) algorithms are an essential tool for sampling from probability distributions. PROC MCMC is useful for these algorithms. However, it is often desirable to code an algorithm from scratch. This is especially present in academia where students are expected to be able to understand and code an MCMC. The ability of SAS^® to accomplish this is relatively unknown yet quite straightforward. We use SAS/IML^® to demonstrate methods for coding an MCMC algorithm with examples of a Gibbs sampler and Metropolis-Hastings random walk.

Texas is one of about 30 states that has recently passed laws requiring voters to produce valid IDs in an effort to avoid illegal voters. This new regulation, however, might negatively affect voting opportunities for students, low-income people, and minorities. To determine the actual effects of the regulation in Dallas County, voters were surveyed when exiting the polling offices during the November midterm election about difficulties that they might have encountered in the voting process. The database of the voting history of each registered voter in the county was examined, and the data set was converted into an analyzable structure prior to stratification. All of the polling offices were stratified by the residents' degrees of involvement in the past three general elections, namely, the proportion of people who have used early election and who have at least voted once. A two-phase sampling design was adopted for stratification. On election day, pollsters were sent to select polling offices and interviewed 20 voters at a selected time period. The number of people having difficulties was estimated when data was collected.

Graduate students often need to explore data and summarize multiple statistical models into tables for a dissertation. The challenges of data summarization include coding multiple, similar statistical models, and summarizing these models into meaningful tables for review. The default method is to type (or copy and paste) results into tables. This often takes longer than creating and running the analyses. Students might spend hours creating tables, only to have to start over when a change or correction in the underlying data requires the analyses to be updated. This paper gives graduate students the tools to efficiently summarize the results of statistical models in tables. These tools include a macro-based SAS/STAT^® analysis and ODS OUTPUT statement to summarize statistics into meaningful tables. Specifically, we summarize PROC GLM and PROC LOGISTIC output. We convert an analysis of hospital-acquired delirium from hundreds of pages of output into three formatted Microsoft Excel files. This paper is appropriate for users familiar with basic macro language.

There is a widely forecast skills gap developing between the numbers of Big Data Analytics (BDA) graduates and the predicted jobs market. Many universities are developing innovative programs to increase the numbers of BDA graduates and postgraduates. The University of Derby has recently developed two new programs that aim to be unique and offer the applicants highly attractive and career-enhancing programs of study. One program is an undergraduate Joint Honours program that pairs analytics with a range of alternative subject areas; the other is a Master's program that has specific emphasis on governance and ethics. A critical aspect of both programs is the synthesis of a Personal Development Planning Framework that enables the students to evaluate their current status, identifies the steps needed to develop toward their career goals, and that provides a means of recording their achievements with evidence that can then be used in job applications. In the UK, we have two sources of skills frameworks that can be synthesized to provide a self-assessment matrix for the students to use as their Personal Development Planning (PDP) toolkit. These are the Skills Framework for the Information Age (SFIA-Plus) framework developed by the SFIA Foundation, and the Student Employability Profiles developed by the Higher Education Academy. A new set of National Occupational Skills (NOS) frameworks (Data Science, Data Management, and Data Analysis) have recently been released by the organization e-Skills UK for consultation. SAS^® UK has had significant input to this new set of NOSs. This paper demonstrates how curricula have been developed to meet the Big Data Analytics skills shortfall by using these frameworks and how these frameworks can be used to guide students in their reflective development of their career plans.

At NC State University, our motto is Think and Do. When it comes to educating students in the Poole College of Management, that means that we want them to not only learn to think critically but also to gain hands-on experience with the tools that will enable them to be successful in their careers. And, in the era of big data, we want to ensure that our students develop skills that will help them to think analytically in order to use data to drive business decisions. One method that lends itself well to thinking and doing is the case study approach. In this paper, we discuss the case study approach for teaching analytical skills and highlight the use of SAS^® software for providing practical, hands-on experience with manipulating and analyzing data. The approach is illustrated with examples from specific case studies that have been used for teaching introductory and intermediate courses in business analytics.

For the Research Data Centers (RDCs) of the United States Census Bureau, the demand for disk space substantially increases with each passing year. Efficiently using the SAS^® data view might successfully address the concern about disk space challenges within the RDCs. This paper discusses the usage and benefits of the SAS data view to save disk space and reduce the time and effort required to manage large data sets. The ability and efficiency of the SAS data view to process regular ASCII, compressed ASCII, and other commonly used file formats are analyzed and evaluated in detail. The authors discuss ways in which using SAS data views is more efficient than the traditional methods in processing and deploying the large census and survey data in the RDCs.

The use of Bayesian methods has become increasingly popular in modern statistical analysis, with applications in numerous scientific fields. In recent releases, SAS^® software has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures in addition to the MCMC procedure, which is designed for general Bayesian modeling. This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then uses examples from the GENMOD and PHREG procedures to describe the built-in Bayesian capabilities, which became available for all platforms in SAS/STAT^® 9.3. Discussion includes how to specify prior distributions, evaluate convergence diagnostics, and interpret the posterior summary statistics.

Long before analysts began mining large data sets, mathematicians sought truths hidden within the set of natural numbers. This exploration was formalized into the mathematical subfield known as number theory. Though this discipline has proven fruitful for many applied fields, number theory delights in numerical truth for its own sake. The austere and abstract beauty of number theory prompted nineteenth century mathematician Carl Friedrich Gauss to dub it The Queen of Mathematics.' This session and the related paper demonstrate that the analytical power of the SAS^® engine is well-suited for exploring concepts in number theory. In Base SAS^®, students and educators will find a powerful arsenal for exploring, illustrating, and visualizing the following: prime numbers, perfect numbers, the Euclidean algorithm, the prime number theorem, Euler's totient function, Chebyshev's theorem, the Chinese remainder theorem, Gauss circle problem, and more! The paper delivers custom SAS procedures and code segments that generate data sets relevant to the exploration of topics commonly found in elementary number theory texts. The efficiency of these algorithms is discussed and an emphasis is placed on structuring data sets to maximize flexibility and ease in exploring new conjectures and illustrating known theorems. Last, the power of SAS plotting is put to use in unexpected ways to visualize and convey number-theoretic facts. This session and the related paper are geared toward the academic user or SAS user who welcomes and revels in mathematical diversions.

During grad school, students learn SAS^® in class or on their own for a research project. Time is limited, so faculty have to focus on what they know are the fundamental skills that students need to successfully complete their coursework. However, real-world research projects are often multifaceted and require a variety of SAS skills. When students transition from grad school to a paying job, they might find that in order to be successful, they need more than the basic SAS skills that they learned in class. This paper highlights 10 insights that I've had over the past year during my transition from grad school to a paying SAS research job. I hope this paper will help other students make a successful transition. Top 10 insights: 1. You still get graded, but there is no syllabus. 2. There isn't time for perfection. 3. Learn to use your resources. 4. There is more than one solution to every problem. 5. Asking for help is not a weakness. 6. Working with a team is required. 7. There is more than one type of SAS^®. 8. The skills you learned in school are just the basics. 9. Data is complicated and often frustrating. 10. You will continue to learn both personally and professionally.

This presentation provides a brief introduction to logistic regression analysis in SAS. Learn differences between Linear Regression and Logistic Regression, including ordinary least squares versus maximum likelihood estimation. Learn to: understand LOGISTIC procedure syntax, use continuous and categorical predictors, and interpret output from ODS Graphics.

Text data constitutes more than half of the unstructured data held in organizations. Buried within the narrative of customer inquiries, the pages of research reports, and the notes in servicing transactions are the details that describe concerns, ideas and opportunities. The historical manual effort needed to develop a training corpus is now no longer required, making it simpler to gain insight buried in unstructured text. With the ease of machine learning refined with the specificity of linguistic rules, SAS Contextual Analysis helps analysts identify and evaluate the meaning of the electronic written word. From a single, point-and-click GUI interface the process of developing text models is guided and visually intuitive. This presentation will walk through the text model development process with SAS Contextual Analysis. The results are in SAS format, ready for text-based insights to be used in any other SAS application.

SAS/ETS provides many tools to improve the productivity of the analyst who works with time series data. This tutorial will take an analyst through the process of turning transaction-level data into a time series. The session will then cover some basic forecasting techniques that use past fluctuations to predict future events. We will then extend this modeling technique to include explanatory factors in the prediction equation.

Learning a new programming language is not an easy task, especially for someone who does not have any programming experience. Learning the SAS^® programming language can be even more challenging. One of the reasons is that the SAS System consists of a variety of languages, such as the DATA step language, SAS macro language, Structured Query Language for the SQL procedure, and so on. Furthermore, each of these languages has its own unique characteristics and simply learning the syntax is not sufficient to grasp the language essence. Thus, it is not unusual to hear about someone who has learned SAS for several years and has never become a SAS programming expert. By using the DATA step language as an example, I would like to share some of my experiences on effectively teaching the SAS language.

There are many pedagogic theories and practices that academics research and follow as they strive to ensure excellence in their students' achievements. In order to validate the impact of different approaches, there is a need to apply analytical techniques to evaluate the changing levels of achievements that occur as a result of changes in applied pedagogy. The analytics used should be easily accessible to all academics with minimal overhead in terms of the collection of new data. This paper is based on a case study of the changing pedagogical approaches of the author over the past five years, using grade profiles from a wide range of modules taught by the author in both the School of Computing and Maths and the Business School at the University of Derby. Base SAS^® and SAS^® Studio were used to evaluate and demonstrate the impact of the change from a pedagogical position of Academic as Domain Expert to a pedagogical position of Academic as Learning-to-Learn Expert . This change resulted in greater levels of research that supported learning along with better writing skills. The application of Learning Analytics in this case study demonstrates a very significant improvement in grade profiles of all students of between 15% and 20%. More surprisingly, it demonstrates that it also eliminates a significant grade deficit in the black and minority ethnic student population, which is typically about 15% in a large number of UK universities.

A common complaint of employers is that educational institutions do not prepare students for the types of messy data and multi-faceted requirements that occur on the job. No organization has data that resembles the perfectly scrubbed data sets in the back of a statistics textbook. The objective of the Annual Report Project is to quickly bring new SAS^® users to a level of competence where they can use real data to meet real business requirements. Many organizations need annual reports for stockholders, funding agencies, or donors. Or, they need annual reports at the department or division level for an internal audience. Being tapped as part of the team creating an annual report used to mean weeks of tedium, poring over columns of numbers in 8-point font in (shudder) Excel spreadsheets, but no more. No longer painful, using a few SAS procedures and functions, reporting can be easy and, dare I say, fun. All analyses are done using SAS^® Studio (formerly SAS^® Web Editor) of SAS OnDemand for Academics. This paper uses an example with actual data for a report prepared to comply with federal grant funding requirements as proof that, yes, it really is that simple.

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Join us for lunch as we discuss the benefits of being part of the elite group that is SAS Certified Professionals. The SAS Global Certification program has awarded more than 79,000 credentials to SAS users across the globe. Come listen to Terry Barham, Global Certification Manager, give an overview of the SAS Certification program, explain the benefits of becoming SAS certified and discuss exam preparation tips. This session will also include a Q&A section where you can get answers to your SAS Certification questions.

In today's competitive job market, both recent graduates and experienced professionals are looking for ways to set themselves apart from the crowd. SAS^® certification is one way to do that. SAS Institute Inc. offers a range of exams to validate your knowledge level. In writing this paper, we have drawn upon our personal experiences, remarks shared by new and longtime SAS users, and conversations with experts at SAS. We discuss what certification is and why you might want to pursue it. Then we share practical tips you can use to prepare for an exam and do your best on exam day.

Are you a SAS^® software user hoping to convince your organization to move to the latest product release? Has your management team asked how your organization can hire new SAS users familiar with the latest and greatest procedures and techniques? SAS^® Studio and SAS^® University Edition might provide the answers for you. SAS University Edition was created for teaching and learning. It's a new downloadable package of selected SAS products (Base SAS^®, SAS/STAT^®, SAS/IML^®, SAS/ACCESS^® Interface to PC Files, and SAS Studio) that runs on Windows, Linux, and Mac. With the exploding demand for analytical talent, SAS launched this package to grow the next generation of SAS users. Part of the way SAS is helping grow that next generation of users is through the interface to SAS University Edition: SAS Studio. SAS Studio is a developmental web application for SAS that you access through your web browser and--since the first maintenance release of SAS 9.4--is included in Base SAS at no additional charge. The connection between SAS University Edition and commercial SAS means that it's easier than ever to use SAS for teaching, research, and learning, from high schools to community colleges to universities and beyond. This paper describes the product, as well as the intent behind it and other programs that support it, and then talks about some successes in adopting SAS University Edition to grow the next generation of users.

This workshop provides hands-on experience using SAS^® Enterprise Miner. Workshop participants will learn to: open a project, create and explore a data source, build and compare models, and produce and examine score code that can be used for deployment.

This workshop provides hands-on experience using SAS^® Forecast Server. Workshop participants will learn to: create a project with a hierarchy, generate multiple forecast automatically, evaluate the forecasts accuracy, and build a custom model.

This workshop provides hands-on experience with SAS^® Visual Statistics. Workshop participants will learn to: move between the Visual Analytics Explorer interface and Visual Statistics, fit automatic statistical models, create exploratory statistical analysis, compare models using a variety of metrics, and create score code.

This workshop provides hands-on experience performing statistical analysis with SAS University Edition and SAS Studio. Workshop participants will learn to: install and setup, perform basic statistical analyses using tasks, connect folders to SAS Studio for data access and results storage, invoke code snippets to import CSV data into SAS, and create a code snippet.

This workshop provides hands-on experience using SAS^® Text Miner. Workshop participants will learn to: read a collection of text documents and convert them for use by SAS Text Miner using the Text Import node, use the simple query language supported by the Text Filter node to extract information from a collection of documents, use the Text Topic node to identify the dominant themes and concepts in a collection of documents, and use the Text Rule Builder node to classify documents having pre-assigned categories.

Data simulation is a fundamental tool for statistical programmers. SAS^® software provides many techniques for simulating data from a variety of statistical models. However, not all techniques are equally efficient. An efficient simulation can run in seconds, whereas an inefficient simulation might require days to run. This paper presents 10 techniques that enable you to write efficient simulations in SAS. Examples include how to simulate data from a complex distribution and how to use simulated data to approximate the sampling distribution of a statistic.

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Becoming one of the best memorizers in the world doesn't happen overnight. With hard work, dedication, a bit of obsession, and with the assistance of some clever analytics metrics, Nelson Dellis was able to climb himself up to the top of the memory rankings in under a year to become the now 3x USA Memory Champion. In this talk, he explains what it takes to become the best at memory, what is involved in such grueling memory competitions, and how analytics helped him get there.