Matrix Algebra Useful for Statistics / Edition 1

Matrix Algebra Useful for Statistics / Edition 1

by Shayle R. Searle, Bobbi Searle, S. R. Searle
     
 

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ISBN-10: 0471866814

ISBN-13: 9780471866817

Pub. Date: 09/02/1982

Publisher: Wiley

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

Overview

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 is intended to teach useful matrix algebra to 'students, teachers, consultants, researchers, and practitioners' in 'statistics and other quantitative methods'.The author concentrates on practical matters, and writes in a friendly and informal style . . . this is a useful and enjoyable book to have at hand."
-Biometrics

This book is an easy-to-understand guide to matrix algebra and its uses in statistical analysis. The material is presented in an explanatory style rather than the formal theorem-proof format. This self-contained text includes numerous applied illustrations, numerical examples, and exercises.

Product Details

ISBN-13:
9780471866817
Publisher:
Wiley
Publication date:
09/02/1982
Series:
Wiley Series in Probability and Statistics Series, #25
Edition description:
Older Edition
Pages:
464
Product dimensions:
6.16(w) x 9.23(h) x 1.07(d)

Table of Contents

Basic Operations.

Special Matrices.

Determinants.

Inverse Matrices.

Rank.

Canonical Forms.

Generalized Inverses.

Solving Linear Equations.

Partitioned Matrices.

Eigenvalues and Eigenvectors.

Miscellanea.

Applications in Statistics.

The Matrix Algebra of Regression Analysis.

An Introduction to Linear Statistical Models.

References.

Index.

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