Multivariate Data Reduction and Discrimination with SAS Software / Edition 1

by Ravindra Khattree, Dayanand N. Naik

Overview

Easy to read and comprehensive, this book presents descriptive multivariate (DMV) statistical methods using real-world problems and data sets. It offers a unique approach to integrating statistical methods, various kinds of advanced data analyses, and applications of the popular SAS software aids. Emphasis is placed on the correct interpretation of output to draw meaningful conclusions in a variety of disciplines and industries.

Product Details

ISBN-13:	9780471323006
Publisher:	Wiley
Publication date:	08/14/2000
Pages:	584
Product dimensions:	8.10(w) x 11.00(h) x 1.50(d)

About the Author

Ravindra Khattree is an Indian-American statistician and professor of statistics at Oakland University. His contribution to the Fountain-Khattree-Peddada Theorem in Pitman measure of closeness is one of the important results of his work. Khattree is the coauthor of two books and has coedited two volumes. Dayanand N. Naik is the author of Multivariate Data Reduction and Discrimination with SAS Software, published by Wiley.

Table of Contents

Basic Concepts for Multivariate Statistics.

Principal Component Analysis.

Canonical Correlation Analysis.

Factor Analysis.

Discriminant Analysis.

Cluster Analysis.

Correspondence Analysis.

Appendix.

References.

Index.

Editorial Reviews

"...a core or supplementary text for graduate or senior undergraduate students, and a reference for researchers and practitioners." (SciTech Book News, Vol. 24, No. 4, December 2000)

From the Publisher

...a core or supplementary text for graduate or senior undergraduate students, and a reference for researchers and practitioners.

SciTech Book News

Statisticians Khattree (Oakland U., Rochester, Michigan) and Naik (Old Dominion U., Norfolk, Virginia) present a core or supplementary text for graduate or senior undergraduate students, and a reference for researchers and practitioners. As a companion to their (1999), they discuss further aspects, especially those in which the emphasis is more on data reduction, description, estimation, discrimination, and summarization. In these cases, it is generally not necessary to assume either multivariate normality or any other probability distribution. They assume readers to have taken a course in applied statistics and be familiar with matrix algebra. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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