Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book more reader friendly than the first edition. Linear Models, Second Edition is written in such a way as to be self-contained for a person with a background in basic statistics, calculus and linear algebra. The text includes numerous applied illustrations, numerical examples, and exercises, now augmented with computer outputs in SAS and R. Also new to this edition is: A greatly improved internal design and format A short introductory chapter to ease understanding of the order in which topics are taken up Discussion of additional topics including multiple comparisons and shrinkage estimators Enhanced discussions of generalized inverses, the MINQUE, Bayes and Maximum Likelihood estimators for estimating variance components Furthermore, in this edition, the second author adds many pedagogical elements throughout the book. These include numbered examples, end-of-example and end-of-proof symbols, selected hints and solutions to exercises available on the book s website, and references to big data in everyday life. Featuring a thorough update, Linear Models, Second Edition includes: A new internal format, additional instructional pedagogy, selected hints and solutions to exercises, and several more real-life applications Many examples using SAS and R with timely data sets Over 400 examples and exercises throughout the book to reinforce understanding Linear Models, Second Edition is a textbook and a reference for upper-level undergraduate and beginning graduate-level courses on linear models, statisticians, engineers, and scientists who use multiple regression or analysis of variance in their work. SHAYLE R. SEARLE, PhD, was Professor Emeritus of Biometry at Cornell University. He was the author of the first edition of Linear Models, Linear Models for Unbalanced Data, and Generalized, Linear, and Mixed Models (with Charles E. McCulloch), all from Wiley. The first edition of Linear Models appears in the Wiley Classics Library. MARVIN H. J. GRUBER, PhD, is Professor Emeritus at Rochester Institute of Technology, School of Mathematical Sciences. Dr. Gruber has written a number of papers and has given numerous presentations at professional meetings during his tenure as a professor at RIT. His fields of interest include regression estimators and the improvement of their efficiency using shrinkage estimators. He has written and published two books on this topic. Another of his books, Matrix Algebra for Linear Models, also published by Wiley, provides good preparation for studying Linear Models. He is a member of the American Mathematical Society, the Institute of Mathematical Statistics and the American Statistical Association.

A thoroughly updated guide to matrix algebra and it uses in statistical analysis and features SAS(R), MATLAB(R), and R throughout This Second Edition addresses matrix algebra that is useful in the statistical analysis of data as well as within statistics as a whole. The material is presented in an explanatory style rather than a formal theorem-proof format and is self-contained. Featuring numerous applied illustrations, numerical examples, and exercises, the book has been updated to include the use of SAS, MATLAB, and R for the execution of matrix computations. In addition, Andre I. Khuri, who has extensive research and teaching experience in the field, joins this new edition as co-author. The Second Edition also: * Contains new coverage on vector spaces and linear transformations and discusses computational aspects of matrices * Covers the analysis of balanced linear models using direct products of matrices * Analyzes multiresponse linear models where several responses can be of interest * Includes extensive use of SAS, MATLAB, and R throughout * Contains over 400 examples and exercises to reinforce understanding along with select solutions * Includes plentiful new illustrations depicting the importance of geometry as well as historical interludes Matrix Algebra Useful for Statistics, Second Edition is an ideal textbook for advanced undergraduate and first-year graduate level courses in statistics and other related disciplines. The book is also appropriate as a reference for independent readers who use statistics and wish to improve their knowledge of matrix algebra. THE LATE SHAYLE R. SEARLE, PHD, was professor emeritus of biometry at Cornell University. He was the author of Linear Models for Unbalanced Data and Linear Models and co-author of Generalized, Linear, and Mixed Models, Second Edition, Matrix Algebra for Applied Economics, and Variance Components, all published by Wiley. Dr. Searle received the Alexander von Humboldt Senior Scientist Award, and he was an honorary fellow of the Royal Society of New Zealand. ANDRE I. KHURI, PHD, is Professor Emeritus of Statistics at the University of Florida. He is the author of Advanced Calculus with Applications in Statistics, Second Edition and co-author of Statistical Tests for Mixed Linear Models, all published by Wiley. Dr. Khuri is a member of numerous academic associations, among them the American Statistical Association and the Institute of Mathematical Statistics.

Coverage of matrix algebra for economists and students of economics

Matrix Algebra for Applied Economics explains the important tool of matrix algebra for students of economics and practicing economists. It includes examples that demonstrate the foundation operations of matrix algebra and illustrations of using the algebra for a variety of economic problems.

The authors present the scope and basic definitions of matrices, their arithmetic and simple operations, and describe special matrices and their properties, including the analog of division. They provide in-depth coverage of necessary theory and deal with concepts and operations for using matrices in real-life situations. They discuss linear dependence and independence, as well as rank, canonical forms, generalized inverses, eigenroots, and vectors. Topics of prime interest to economists are shown to be simplified using matrix algebra in linear equations, regression, linear models, linear programming, and Markov chains.

Highlights include:

• Numerous examples of real-world applications
• Challenging exercises throughout the book
• Mathematics understandable to readers of all backgrounds
• Extensive up-to-date reference material

Matrix Algebra for Applied Economics provides excellent guidance for advanced undergraduate students and also graduate students. Practicing economists who want to sharpen their skills will find this book both practical and easy-to-read, no matter what their applied interests.

This 1971 classic on linear models is once again available—as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

." . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models."
--Journal of the American Statistical Association

"This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation."
--Mathematical Reviews

"The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation."
--Technometrics

This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.

WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
" This book] provides an excellent discussion of the methodology and interpretation of linear models analysis of unbalanced data (data having unequal numbers of observations in the subclasses), generally without matrices?the author does an excellent job of emphasizing the more practical nature of the book. Highly recommended for graduate and undergraduate libraries."
"Choice
"This is a very comprehensive text, aimed at both students studying linear-model theory and practicing statisticians who require an understanding of the model-fitting procedures incorporated in statistical packages?This book should be considered as a text for college courses as it provides a clearly presented and thorough treatment of linear models. It will also be useful to any practicing statistician who has to analyze unbalanced data, perhaps arising from surveys, and wishes to understand the output from model-fitting procedures and the discrepancies in analysis from one recognized package to another."
"Biometrics
This newly available and affordably priced paperback version of Linear Models for Unbalanced Data offers a presentation of the fundamentals of linear statistical models unique in its total devotion to unbalanced data and its emphasis on the up-to-date cell means model approach to linear models for unbalanced data. Topic coverage includes cell means models, 1-way classification, nested classifications, 2-way classification with some-cells-empty data, models with covariables, matrix algebra and quadratic forms, linear model theory, and much more.