Matrix Algebra Useful for Statistics

by Shayle R. Searle & Andre I. Khuri
$169.99
eBook

Publisher: Wiley

Series: Wiley Series in Probability and Statistics

Publication Date: April 11, 2017

ISBN: 9781118935156

Binding: Kobo eBook

Availability: eBook

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A thoroughly updated guide to matrix algebra and it uses in statistical analysis and features SAS®, MATLAB®, 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, André 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, ...