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Applied Multivariate Statistical Analysis (5th Edition)
 
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Applied Multivariate Statistical Analysis (5th Edition) [Hardcover]

Richard A. Johnson (Author), Dean W. Wichern (Author)
3.4 out of 5 stars  See all reviews (28 customer reviews)


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Applied Multivariate Statistical Analysis (6th Edition) Applied Multivariate Statistical Analysis (6th Edition) 3.4 out of 5 stars (28)
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Book Description

0130925535 978-0130925534 December 6, 2001 5

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.



Editorial Reviews

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.

Product Details

  • Hardcover: 767 pages
  • Publisher: Prentice Hall; 5 edition (December 6, 2001)
  • Language: English
  • ISBN-10: 0130925535
  • ISBN-13: 978-0130925534
  • Product Dimensions: 9.2 x 7.5 x 1.4 inches
  • Shipping Weight: 3.2 pounds
  • Average Customer Review: 3.4 out of 5 stars  See all reviews (28 customer reviews)
  • Amazon Best Sellers Rank: #892,648 in Books (See Top 100 in Books)

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Customer Reviews

28 Reviews
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Average Customer Review
3.4 out of 5 stars (28 customer reviews)
 
 
 
 
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49 of 50 people found the following review helpful:
4.0 out of 5 stars Book Contents, March 10, 2006
By 
MAURICIO AGUIAR (Rio de Janeiro, RJ, Brazil) - See all my reviews
(REAL NAME)   
This review is from: Applied Multivariate Statistical Analysis (5th Edition) (Hardcover)
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.
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44 of 45 people found the following review helpful:
5.0 out of 5 stars A Students Review, February 6, 2002
By 
Hrafn (Denver, CO) - See all my reviews
First: I must prefix this by saying that I am majoring in the Mathematical and Computer Sciences.

This semester I decided to take a class that happened to use this text as its source. I have been extremely pleased with it: the theoretical work is excellent, the proofs are thourough, the exercises are both good and cover a broad variety of difficulties, and the tables on the CD provide excellent experience in analyzing real world data.

A couple of things to keep in mind before you purchase this book, however:

1) A good background in linear algebra and basic statistics is highly recommended and virtually necessary to interpret this book. Remembering the knowledge gleaned from "Sequences and Series" (often taught in Calculus II) will also prove useful. The text is good, but it is often nontrivial.

2) Some kind of software that does multivariate analysis (and if nothing else, will find eigenvalues and orthonormal eigenvectors) is necessary to get the most out of this book. The software package SAS is touched on in the book, but by no means is given a comprehensive review. However, the data files on the CD-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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32 of 34 people found the following review helpful:
4.0 out of 5 stars excellent book, February 12, 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.
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