SAS for Linear Models, Fourth Edition - Softcover

Littell, Ramon; Stroup, Walter; Freund, Rudolf

Softcover

ISBN 10: 1590470230 ISBN 13: 9781590470237

Publisher: SAS Institute, 2002

Synopsis

This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material.

This book is part of the SAS Press program.

About this title

About the Author

Ramon C. Littell, Ph.D., Professor of Statistics at the University of Florida, is the coauthor of several books, including SAS System for Regression, Third Edition, and SAS for Linear Models, Fourth Edition. He has worked with SAS software since 1986.

Walter W. Stroup, Ph.D., is currently professor in the Department of Statistics at the University of Nebraska. His responsibilities include teaching statistical modeling, design of experiments, and research applications of mixed models in collaboration with researchers in agriculture, natural resources, medical and pharmaceutical sciences, education, and the behavioral sciences. Dr. Stroup received a B.A. degree in psychology from Antioch College, and M.S. and Ph.D. degrees in statistics from the University of Kentucky. He is widely published in statistical and applied journals and has participated in a number of symposia on issues in statistical consulting and statistical modeling. He has taught numerous short courses and workshops on generalized linear mixed models. A SAS user since 1975, Dr. Stroup is coauthor of SAS for Linear Models, Fourth Edition, SAS for Mixed Models, both editions. In additional, Dr. Stroup is author of Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, an introduction to GLMMs that makes extensive use of SAS examples.

Rudolf J. Freund, Ph.D. former associate director and cofounder of the Department of Statistics at Texas A & M University, is now professor emeritus. Dr. Freund received an M.A. degree in economics from the University of Chicago in 1951 and a Ph.D. in statistics from North Carolina State College (now North Carolina State University) in 1955. He has coauthored several books including SAS System for Regression, Third Edition; Regression Methods; and Statistical Methods and Regression Analysis. Dr. Freund has been a SAS user since 1972, and is a former SUGI chairman.

Review

The major enhancement in the fourth edition involves the addition of substantial information and detailed examples on mixed models using PROC MIXED as well as thorough comparisons of PROC MIXED and PROC GLM analyses. It serves as an excellent introduction to PROC MIXED for the most common mixed-models situations (nested, two-way cross-classifications, split-plots, models with mixtures of crossed and nested effects and repeated measures), using classical random-effects assumptions. There are good discussions about random versus fixed effects, problems with unbalanced date or missing cells, techniques or analysis. --Leigh W. Murray, University Statistics Center

The authors aim to write a book that offers a broad coverage of regression and ANOVA models. They have completed the mission. The first chapter, 'Introduction,' is clear and should be read first to get a sense of the road map to the linear models. Because of the breadth rather than depth of its content, it fits intermediate users; however, advanced users may use it for quick reference. That is, this book is good for an overview as well as a reference. The whole book is user friendly, and it is easy to follow the content. Its special feature is the comparison of current advancements in selecting methods (such as PROC ANOVA and PROC GLM) for analyzing linear models. I highly recommend this book for ANOVA/SAS courses. --Mayling M. Chu, Ph.D., California State University, Stanislaus

This is a book for the statistically sophisticated SAS software user. The coverage is quite broad, starting with a brief review of basic regression ideas and extending through mixed models and generalized linear models, including Poisson models, logistic models, models that use quasi-likelihood and generalized estimating equations. Advanced concepts are presented in a user-friendly way and interesting relevant examples are presented. --David A. Dickey, North Carolina State University

The authors aim to write a book that offers a broad coverage of regression and ANOVA models. They have completed the mission. The first chapter, 'Introduction,' is clear and should be read first to get a sense of the road map to the linear models. Because of the breadth rather than depth of its content, it fits intermediate users; however, advanced users may use it for quick reference. That is, this book is good for an overview as well as a reference. The whole book is user friendly, and it is easy to follow the content. Its special feature is the comparison of current advancements in selecting methods (such as PROC ANOVA and PROC GLM) for analyzing linear models. I highly recommend this book for ANOVA/SAS courses. --Mayling M. Chu, Ph.D., California State University, Stanislaus

This is a book for the statistically sophisticated SAS software user. The coverage is quite broad, starting with a brief review of basic regression ideas and extending through mixed models and generalized linear models, including Poisson models, logistic models, models that use quasi-likelihood and generalized estimating equations. Advanced concepts are presented in a user-friendly way and interesting relevant examples are presented. --David A. Dickey, North Carolina State University

The authors aim to write a book that offers a broad coverage of regression and ANOVA models. They have completed the mission. The first chapter, 'Introduction,' is clear and should be read first to get a sense of the road map to the linear models. Because of the breadth rather than depth of its content, it fits intermediate users; however, advanced users may use it for quick reference. That is, this book is good for an overview as well as a reference. The whole book is user friendly, and it is easy to follow the content. Its special feature is the comparison of current advancements in selecting methods (such as --David A. Dickey, North Carolina State University

The authors aim to write a book that offers a broad coverage of regression and ANOVA models. They have completed the mission. The first chapter, 'Introduction,' is clear and should be read first to get a sense of the road map to the linear models. Because of the breadth rather than depth of its content, it fits intermediate users; however, advanced users may use it for quick reference. That is, this book is good for an overview as well as a reference. The whole book is user friendly, and it is easy to follow the content. Its special feature is the comparison of current advancements in selecting methods (such as PROC ANOVA and PROC GLM) for analyzing linear models. I highly recommend this book for ANOVA/SAS courses. --Mayling M. Chu, Ph.D., California State University, Stanislaus

This is a book for the statistically sophisticated SAS software user. The coverage is quite broad, starting with a brief review of basic regression ideas and extending through mixed models and generalized linear models, including Poisson models, logistic models, models that use quasi-likelihood and generalized estimating equations. Advanced concepts are presented in a user-friendly way and interesting relevant examples are presented. --David A. Dickey, North Carolina State University

Edition details

• Publisher: SAS Institute
• Publication date: 2002
• ISBN 10: 1590470230
• ISBN 13: 9781590470237
• Binding: Paperback
• Language: English
• Edition number: 4
• Number of pages: 492