Revised to reflect the current developments on the topic, Linear Statistical Models, Second Edition provides an up-to-date approach to various statistical model concepts. The book includes clear discussions that illustrate key concepts in an accessible and interesting format while incorporating the most modern software applications.

This Second Edition follows an introduction-theorem-proof-examples format that allows for easier comprehension of how to use the methods and recognize the associated assumptions and limits. In addition to discussions on the methods of random vectors, multiple regression techniques, simultaneous confidence intervals, and analysis of frequency data, new topics such as mixed models and curve fitting of models have been added to thoroughly update and modernize the book. Additional topical coverage includes:

An introduction to R and S-Plus� with many examples
Multiple comparison procedures
Estimation of quantiles for regression models
An emphasis on vector spaces and the corresponding geometry

Extensive graphical displays accompany the book's updated descriptions and examples, which can be simulated using R, S-Plus�, and SAS� code. Problems at the end of each chapter allow readers to test their understanding of the presented concepts, and additional data sets are available via the book's FTP site.

Linear Statistical Models, Second Edition is an excellent book for courses on linear models at the upper-undergraduate and graduate levels. It also serves as a comprehensive reference for statisticians, engineers, and scientists who apply multiple regression or analysis of variance in their everyday work.

About the Author

James H. Stapleton, PhD, is Professor Emeritus in the Department of Statistics and Probability at Michigan State University. He is the author of Models for Probability and Statistical Inference: Theory and Applications, also published by Wiley.

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