Exploring Modern Regression Methods Using SAS

Exploring Modern Regression Methods Using SAS
Author:
Publisher:
Total Pages: 142
Release: 2019-06-21
Genre:
ISBN: 9781642954876

This special collection of SAS Global Forum papers demonstrates new and enhanced capabilities and applications of lesser-known SAS/STAT and SAS Viya procedures for regression models. The goal here is to raise awareness of current valuable SAS/STAT content of which the user may not be aware. Also available free as a PDF from sas.com/books.

Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R
Author: Olga Korosteleva
Publisher: CRC Press
Total Pages: 325
Release: 2018-12-07
Genre: Mathematics
ISBN: 1351690086

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.

Exploring SAS Viya

Exploring SAS Viya
Author: Sas Education
Publisher:
Total Pages: 80
Release: 2019-06-14
Genre:
ISBN: 9781642954838

This first book in the series covers how to access data files, libraries, and existing code in SAS Studio. You also learn about new procedures in SAS Viya, how to write new code, and how to use some of the pre-installed tasks that come with SAS Visual Data Mining and Machine Learning. In the last chapter, you learn how to use the features in SAS Data Preparation to perform data management tasks using SAS Data Explorer, SAS Data Studio, and SAS Lineage Viewer. Also available free as a PDF from sas.com/books.

A Modern Approach to Regression with R

A Modern Approach to Regression with R
Author: Simon Sheather
Publisher: Springer Science & Business Media
Total Pages: 398
Release: 2009-02-27
Genre: Mathematics
ISBN: 0387096086

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.

Exploring SAS Viya

Exploring SAS Viya
Author: Sas Education
Publisher:
Total Pages: 126
Release: 2020-01-10
Genre: Computers
ISBN: 9781642955880

SAS Visual Data Mining and Machine Learning, powered by SAS Viya, means that users of all skill levels can visually explore data on their own while drawing on powerful in-memory technologies for faster analytic computations and discoveries. You can manually program with custom code or use the features in SAS Studio, Model Studio, and SAS Visual Analytics to automate your data manipulation and modeling. These programs offer a flexible, easy-to-use, self-service environment that can scale on an enterprise-wide level. In this book, we will explore some of the many features of SAS Visual Data Mining and Machine Learning including: programming in the Python interface; new, advanced data mining and machine learning procedures; pipeline building in Model Studio, and model building and comparison in SAS Visual Analytics.

Design and Analysis of Experiments

Design and Analysis of Experiments
Author: Leonard C. Onyiah
Publisher: CRC Press
Total Pages: 852
Release: 2008-07-29
Genre: Mathematics
ISBN: 1420060554

Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory, it also explores regression approaches. Capitalizing on the availability of cutting-edge software, the author uses both manual meth

Regression & Linear Modeling

Regression & Linear Modeling
Author: Jason W. Osborne
Publisher: SAGE Publications
Total Pages: 489
Release: 2016-03-24
Genre: Psychology
ISBN: 1506302750

In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Discovering Statistics Using SAS

Discovering Statistics Using SAS
Author: Andy Field
Publisher: SAGE
Total Pages: 753
Release: 2010-01-30
Genre: Reference
ISBN: 1446242234

Hot on the heels of the 3rd edition of Andy Field′s award-winning Discovering Statistics Using SPSS comes this brand new version for students using SAS®. Andy has teamed up with a co-author, Jeremy Miles, to adapt the book with all the most up-to-date commands and programming language from SAS® 9.2. If you′re using SAS®, this is the only book on statistics that you will need! The book provides a comprehensive collection of statistical methods, tests and procedures, covering everything you′re likely to need to know for your course, all presented in Andy′s accessible and humourous writing style. Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS®. A ′cast of characters′ supports the learning process throughout the book, from providing tips on how to enter data in SAS® properly to testing knowledge covered in chapters interactively, and ′real world′ and invented examples illustrate the concepts and make the techniques come alive. The book′s companion website (see link above) provides students with a wide range of invented and real published research datasets. Lecturers can find multiple choice questions and PowerPoint slides for each chapter to support their teaching.

Introduction to Statistical and Machine Learning Methods for Data Science

Introduction to Statistical and Machine Learning Methods for Data Science
Author: Carlos Andre Reis Pinheiro
Publisher: SAS Institute
Total Pages: 169
Release: 2021-08-06
Genre: Computers
ISBN: 1953329624

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.