Matrix Algebra Useful For Statistics, Second Edition

The purpose of this book is to bring a broad spectrum of readers’ knowledge of matrix algebra that is useful in the statistical analysis of data and in statistics generally. The material is presented in an explanatory style rather than the formal theorem-proof format. Written in such a way as to be self-contained, this text includes numerous applied illustrations, numerical examples, and exercises, now augmented in its first revision in over 30 years with computer discussions of SAS, Matlab, and R. Also new to this edition is the addition of a co-author who has had extensive research and teaching experience in the subject matter; a slightly reorganized chapter sequence to ease discussion; historical interludes when appropriate; more applied real-time examples; and selected hints and solutions to exercises at the rear of the book.

Andre I. Khuri, PhD, is Professor Emeritus of Statistics at the University of Florida. He has published several books in the field of linear models and advanced calculus with applications in statistics, two of which were published by Wiley. Dr. Khuri is a member of numerous academic associations, among them the American Statistical Association and the Institute of Mathematical Statistics.

The late Shayle R. Searle, PhD, was professor emeritus of biometry at Cornell University. He was the author of Variance Components, Linear Models and Linear Models for Unbalanced Data ?and co-author of Generalized, Linear, and Mixed Models, 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.