This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, the authors 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.
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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
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
About the Author
Ramon C. Littell, Ph.D. is Professor of Statistics at the University of Florida, where he teaches applied statistics and serves as a consulting statistician at the Institute of Food and Agricultural Sciences. Dr. Littell is coauthor of the three previous editions of this book with Dr. Freund and is also widely published in statistical and applied journals. He is also coauthor of SAS System for Regression, Third Edition; SAS System for Mixed Models; and SAS System for Elementary Statistical Analysis, Second Edition. A SAS user since 1972, Dr. Littell has served as SUGI chairman and is a Fellow of the American Statistical Association.
Walter W. Stroup, Ph.D. is currently head of the Department of Biometry at the University of Nebraska, where he also holds a teaching, research, and consulting appointment at the Institute of Agriculture and Natural Resources. 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. A SAS user since 1975, Dr. Stroup is coauthor of SAS System for Mixed Models.
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.
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Most Helpful Customer Reviews
26 of 26 people found the following review helpful:
5.0 out of 5 stars
Nice primer on linear models,
By
This review is from: SAS for Linear Models (Paperback)
This is the fourth edition of this primer on linear models by Littell and Freund. As with the previous editions it gives a thorough treatment of the models and their application through the use of SAS procedures. In the fourth edition, Walter Stroup has been added and Phil Spector dropped as coauthor. Also this edition drops the word "System" in the title after "SAS". The main addition to this edition is the inclusion of a lot of detailed information on the application of mixed effects models through use of the MIXED procedure. There is a great deal of comparison of the differences between PROC MIXED and PROC GLM in the analysis of these models. Many interesting examples are presented and care is taken to show the user how to specify the models so as to get the appropriate analysis. They also teach you enough to aid in interpreting the output.
Littell, Milliken, Stoup and Wofinger have also written a very nice book titled "The SAS System for Mixed Models" and my only question would be to ask "which book offers more?" This fourth edition seems to now cover many of the same topics that highlight that book. Readers should be aware of the two books and should investigate for themselves the differences and overlap before deciding to purchase either one. One clear difference is the date of publication. This book, published in 2002, is more current and has several references from 1997 and after whereas the mixed models book was published in 1996.
13 of 24 people found the following review helpful:
2.0 out of 5 stars
Absolutely useless for economists and finance pros,
By
This review is from: SAS for Linear Models, Fourth Edition (Paperback)
I give this book two stars from an econometrician's point of view. This book focuses on linear models related to ANOVA and hence may have a good audience in disciplines such as statistics, biostatistics, etc, but it has ABSOLUTELY nothing useful for people interested in econometrics or financial statistics.
For example, the book doesn't even mention heteroskedasticity at all, and there's absolutely no time series coverage. In econometrics we also do weighted regressions a lot, and this book doesn't have anything. I wish the book title had been more descriptive. Linear models are many stripes and used in many different disciplines. By using such a catch-call title, the book is misleading. So, if you want to do linear models in economics or finance, do NOT buy this book.
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Inside This Book
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First Sentence:
The fourth edition of SAS for Linear Models, like earlier editions, plays a role somewhere between a textbook on applied linear models and a manual for using certain procedures in SAS. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
insect count data, simple effect comparisons, drug trt, random variables representing differences, block trt, more other fixed effects, using expected mean squares, proc glm, blk trt, lsmeans trt, analyzing unbalanced data, study trt, proc anova, residual log likelihood, estimable functions, proc mixed, proc genmod, reduction notation, drug std, proc ttest, proc reg, comparisonwise error rate, preplanned comparisons, thermal distress, multiway classifications Key Phrases - Capitalized Phrases (CAPs): (learn more)
Corrected Total, Sum of Source, Standard Parameter Estimate Error, R-Square Coeff Var Root, Mean Std Dev, Pearson Chi-Square, Roy's Greatest Root, Delta Rule, Least Squares Means Standard Effect, Source Type, Value Value, Covariance Parameter Estimates Cov Parm Estimate, Hotelling-Lawley Trace, Statistic Value, Model Information, Pillars Trace, Itl Intercept, Root Percent, Value Num, Adj R-sq, Analysis Of Parameter Estimates Standard Wald, Error Term Source, Interpreting Sums of Squares, Contrasts Num Den Label, Two-Way Factorial Experiment New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The fourth edition of SAS for Linear Models, like earlier editions, plays a role somewhere between a textbook on applied linear models and a manual for using certain procedures in SAS. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
insect count data, simple effect comparisons, drug trt, random variables representing differences, block trt, more other fixed effects, using expected mean squares, proc glm, blk trt, lsmeans trt, analyzing unbalanced data, study trt, proc anova, residual log likelihood, estimable functions, proc mixed, proc genmod, reduction notation, drug std, proc ttest, proc reg, comparisonwise error rate, preplanned comparisons, thermal distress, multiway classifications Key Phrases - Capitalized Phrases (CAPs): (learn more)
Corrected Total, Sum of Source, Standard Parameter Estimate Error, R-Square Coeff Var Root, Mean Std Dev, Pearson Chi-Square, Roy's Greatest Root, Delta Rule, Least Squares Means Standard Effect, Source Type, Value Value, Covariance Parameter Estimates Cov Parm Estimate, Hotelling-Lawley Trace, Statistic Value, Model Information, Pillars Trace, Itl Intercept, Root Percent, Value Num, Adj R-sq, Analysis Of Parameter Estimates Standard Wald, Error Term Source, Interpreting Sums of Squares, Contrasts Num Den Label, Two-Way Factorial Experiment New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
See all 100 books this book cites
- An Introduction to Categorical Data Analysis by Alan Agresti in Back Matter (1), and Back Matter (2)
- Aspects of Statistical Inference (Wiley Series in Probability and Statistics) by A. H. Welsh in Back Matter
- Case Studies in Biometry by Nicholas Lange in Back Matter
- Bootstrap Methods: A Guide for Practitioners and Researchers (Wiley Series in Probability and Statistics) by Michael R. Chernick in Back Matter
- Statistical Methods in Diagnostic Medicine (Wiley Series in Probability and Statistics) by Xiao-Hua Zhou in Back Matter
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