Methods of Multivariate Analysis

by Rencher, Alvin C.

• ISBN13: 9780471271352
• ISBN10: 0471271357
• Edition: 2nd
• Format: eBook
• Copyright: 2003-04-11
• Publisher: Wiley-Interscience

Summary

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen.

When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline.

To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on:

• Cluster analysis
• Multidimensional scaling
• Correspondence analysis
• Biplots

Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.

Author Biography

ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Linear Models in Statistics and Multivariate Statistical Inference and Applications, both available from Wiley.

Table of Contents

Introduction.

Matrix Algebra.

Characterizing and Displaying Multivariate Data.

The Multivariate Normal Distribution.

Tests on One or Two Mean Vectors.

Multivariate Analysis of Variance.

Tests on Covariance Matrices.

Discriminant Analysis: Description of Group Separation.

Classification Analysis: Allocation of Observations to Groups.

Multivariate Regression.

Canonical Correlation.

Principal Component Analysis.

Factor Analysis.

Cluster Analysis.

Graphical Procedures.

Tables.

Answers and Hints to Problems.

Data Sets and SAS Files.

References.

Index.