Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author: Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.

Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. - A complete framework for the data preparation process, including implementation details for each step. - The complete SAS implementation code, which is readily usable by professional analysts and data miners. - A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. - Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.

« Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--

Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

TAGLINE Elevate Your Data Analytics Skills, Optimize Workflows, and Drive Informed Decision-Making Across the Spectrum of Data Professions! KEY FEATURES ● Solve practical problems using SAS with real-world case studies that provide hands-on experience. ● Clear, step-by-step tutorials that guide you through various SAS procedures, ensuring easy understanding and application. ● Explore an extensive range of SAS capabilities, from basic data management to advanced analytics and reporting techniques. DESCRIPTION The "Ultimate Statistical Analysis System (SAS) for Data Analytics" is your go-to resource for mastering SAS, a powerful software suite for statistical analysis. This comprehensive book covers everything from the basics of SAS for data professionals to advanced topics like clustering analysis and association rules. With practical examples and clear explanations, this book equips readers with the knowledge and skills needed to excel in their roles as data scientists, analysts, researchers, and more. Whether you're a beginner looking to build a solid foundation in SAS or an experienced user seeking to expand your proficiency, this handbook has something for everyone. You'll learn essential techniques for importing, cleaning, and visualizing data, as well as conducting hypothesis testing, regression analysis, and inferential statistics. Advanced topics like SAS programming concepts and generating reports are also covered in detail, providing readers with the tools to tackle complex data challenges with confidence. With its accessible writing style and emphasis on real-world applications, this book is a practical guide that empowers readers to unlock the full potential of their data. Whether you're analyzing customer behavior, optimizing business processes, or conducting academic research, this handbook will be your trusted companion on the journey to mastering SAS and making informed decisions based on data-driven insights. WHAT WILL YOU LEARN ● Master the skills to import, clean, and transform data using SAS's powerful data manipulation tools. ● Gain the ability to conduct hypothesis testing to build regression models to analyze data relationships. ● Learn to design and produce compelling data visualizations that effectively communicate your data findings. ● Develop proficiency in advanced SAS programming techniques to tackle intricate analytical tasks. ● Discover the use of clustering analysis and association rules to identify meaningful patterns and relationships in your data. ● Generate professional reports to clearly present your analytical results. WHO IS THIS BOOK FOR? This book is ideal for data professionals, analysts, researchers, and anyone seeking to enhance their statistical analysis skills with SAS. Prior familiarity with basic statistical concepts and some experience with data analysis tools would be beneficial for readers to fully leverage the content of this handbook. TABLE OF CONTENTS 1. Introduction to SAS for Data Professionals 2. Data Import and Export in SAS 3. Data Cleaning and Transformation 4. Data Visualizations with SAS 5. Hypothesis Testing and Regression Analysis 6. Descriptive and Inferential Statistics 7. Advanced SAS Programming Concepts 8. Clustering Analysis with PROC CLUSTER 9. Association Rules in SAS 10. Generating Reports in SAS Index

Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R. For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and provides exercises at the end of each chapter. For students and public health professionals, not only is it a gentle introduction to R, but it can serve as a guide to developing the results for a research report using R software. Features: Gives examples in both SAS and R Demonstrates descriptive statistics as well as linear and logistic regression Provides exercise questions and answers at the end of each chapter Uses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 data Guides the reader on producing a health analysis that could be published as a research report Gives an example of hypothesis-driven data analysis Provides examples of plots with a color insert

Gain Powerful Insights with SAS Viya! Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.

Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user’s guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model’s effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.