Applied Multivariate Statistical Analysis, Fifth Edition
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Applied Multivariate Statistical Analysis, Fifth Edition
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From the Publisher
A classic applied approach to multivariate analysis for those involved in the social, biological, and physical sciences.
--This text refers to an out of print or unavailable edition of this title.
From the Back Cover
This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification. For experimental scientists in a variety of disciplines.
Michael R. Chernick statman31147 (Holland PA) | 
12/02/2008
12/02/2008
There have been many good theoretical texts on multivariate analysis including Anderson, Eaton and Gnandesikan. Tabachnick has written a popular applied text for the social sciences. Yet for many years this has been considered the best applied text. That is because the authors understand the theory and know how to balance it with applications. They also are excellent writers.
MAURICIO AGUIAR (Rio de Janeiro, RJ, Brazil) |
10/03/2006
10/03/2006
The "search inside this book" feature was not available when this review was posted. Hope it helps.
CONTENTS
I. GETTING STARTED.
1. Aspects of Multivariate Analysis.
2. Matrix Algebra and Random Vectors.
3. Sample Geometry and Random Sampling.
4. The Multivariate Normal Distribution.
II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
5. Inferences About a Mean Vector.
6. Comparisons of Several Multivariate Means.
7. Multivariate Linear Regression Models.
III. ANALYSIS OF A COVARIANCE STRUCTURE.
8. Principal Components.
9. Factor Analysis and Inference for Structured Covariance Matrices.
10. Canonical Correlation Analysis
IV. CLASSIFICATION AND GROUPING TECHNIQUES.
11. Discrimination and Classification.
12. Clustering, Distance Methods and Ordination.
Appendix.
Data Index.
Subject Index.
Hrafn (Denver, CO) | 
06/02/2002
06/02/2002
First: I must prefix this ROM should be loadable by any competant software package, so use the one you are most comfortable with.
If not overly familiar with any of them, I can recommend S, SPlus, and "GNU's S" (also known as "R") for their power and flexability to work with the data presented in the book.
All and all I found this to be an excellent book, definantly worthwhile if you want or need to know how to do multivariate analysis.
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