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Applied Multivariate Statistical Analysis (6th Edition) Hardcover – April 2, 2007

ISBN-13: 000-0131877151 ISBN-10: 0131877151 Edition: 6th

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Product Details

  • Hardcover: 800 pages
  • Publisher: Pearson; 6 edition (April 2, 2007)
  • Language: English
  • ISBN-10: 0131877151
  • ISBN-13: 978-0131877153
  • Product Dimensions: 7.4 x 1.8 x 9.3 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 3.6 out of 5 stars  See all reviews (45 customer reviews)
  • Amazon Best Sellers Rank: #413,182 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

Dean W. Wichern is Professor Emeritus at the Mays School of Business at Texas A&M University. He holds membership in the American Statistical Association, Royal Statistical Society, International Institute of Forecasters, and Institute for Operations Research and the Management Sciences. He is the author for four textbooks and was Associate Editor of  Journal of Business and Economic Statistics from 1983-1991.

 

                  

Professor Richard A. Johnson is Professor in the Department of Statistics at the University of Wisconsin. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association and he is amember of the Royal Statistical Society and International Statistical Institute. He is the author of six textbooks and over 120 technical publications and is the founding Editor of Statistics and Probability Letters (1981-).

Customer Reviews

You'll need to skim through hundereds of pages that aren't referenced in the index.
N. Wiman
This book was written for anyone with a firm grasp of linear algebra, Calculus, and probability and statistics.
TomT
This book is well laid out and has A LOT of information in it that is very detailed.
jomo

Most Helpful Customer Reviews

65 of 68 people found the following review helpful By MAURICIO AGUIAR on March 10, 2006
Format: 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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50 of 52 people found the following review helpful By Hrafn on February 6, 2002
Format: Hardcover
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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35 of 37 people found the following review helpful By wiredweird HALL OF FAMETOP 1000 REVIEWER on September 10, 2003
Format: Hardcover
People complain that statistics is about memorizing a bunch of formulas and when to use them. I disagree. The real problem is that the formulas almost never apply exactly to the subject at hand - they have to be adjusted to each application.
That's why this book is so helpful. It gives huge numbers of results I can use immediately, but also shows me where they came from. That means that I can rephrase the formulas as needed in special computing environments, but still be sure that I'm getting a meaningful answer. The book has a secondary emphasis that I value very highly: checks that the techniques are giving meaningful answers. It's pretty silly, and perhaps dangerous, to apply a technique without knowing how good its results are. This book gives me the checks I need to measure the quality of the results from each technique.
No, there's no C code to cut and paste. This is a math book. The math is clear and well-developed, though, and mostly limited to linear algebra. As I type this, I have the book open to "Discrimination and Classification," and I have my protoype C program on the screen. That's how directly applicable the book is.
I admit, I haven't appplied every technique in the book. All of the book is equally well-written, though. When I need principal components or basic clustering, this is the book I'll grab first.
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33 of 35 people found the following review helpful By Michael R. Chernick on February 12, 2008
Format: Hardcover
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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15 of 15 people found the following review helpful By R. Kocer on May 6, 2010
Format: Hardcover
I have an older edition but I like it so much I also will acquire the latest edition. The reason is simple:
This is the best book available for multivariate statistical analysis(Of course, if you are not satisfied with cookbooks and click-softwares)
The basic rule of any kind of statistical analysis is this: the computer programmes should be used only for gaining time. If there is anything which you cannot estimate (in principle) by yourself, do not use computer for it. First learn how to do it by hand, go through the theory. Only when you are convinced that you exactly know "why" and "how", then use a software. Of course this kind of a statistical practise is only possible if you have very clear books which explain and prove the theorems and methods. The book by Johnson and Wichern is certainly a perfect book from this perspective.
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18 of 20 people found the following review helpful By "sin4652" on March 16, 2002
Format: Hardcover
this is an ideal text for advanced statistics students who would like to learn about factor analysis, canonical correlations, principal components analysis, linear discriminants, etc... you definitely need to have some background in linear algebra --reading the first few sections will not be sufficient. the examples are better than those found in mardia's book. knowing splus (matlab or similar software) will help you get through the exercises. IT'S A GOOD BOOK TO ADD TO YOU LIBRARY
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