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Applied Multivariate Statistical Analysis (6th Edition), by Richard A. Johnson, Dean W. Wichern
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This market leader offers a readable introduction to the statistical analysis of multivariate observations. Gives readers the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Starts with a formulation of the population models, delineates the corresponding sample results, and liberally illustrates everything with examples. Offers an abundance of examples and exercises based on real data. Appropriate for experimental scientists in a variety of disciplines.
- Sales Rank: #356416 in Books
- Published on: 2007-04-02
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x 1.30" w x 7.20" l, 2.85 pounds
- Binding: Hardcover
- 800 pages
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-).
Most helpful customer reviews
2 of 2 people found the following review helpful.
Excellent reference for learning classical (and some other) multivariate techniques
By Amazon Customer
This book is essential for any researcher - those who need to understand the mathematical underpinnings of various multivariate techniques such as multivariate linear regression, principal components, factor analysis, clustering techniques (decision trees). It thoroughly covers the basics of linear algebra (matrix math, calculating eigenvalues/vectors/...) before going into detail on the multivariate normal distribution which is often used for inferential purposes in multivariate regression, principal components, factor analysis, and some classification/clustering techniques. The authors clearly show how the math works behind these popular multivariate techniques.
There are some reviews that point out the endless formulas and proofs in this book, and I agree that the book is slanted to those with a mathematical background. If you've taken calculus courses and a linear algebra course (although they do review important linear algebra topics) in college then you shouldn't have too much of a problem understanding this book, as the authors have done an excellent job in being as lucid as one can in teaching topics like this. Also, there are plenty of examples, and the authors usually end each chapter with a check list of how to apply the techniques, and discuss how graphical tools (dot charts, chi-square plots) can be used to assess the appropriateness of the statistical technique. Each chapter has an equal set of applied and proof problems, catering both to applied and theoretical researchers.
In short, I would say that the writing style is similar to Kutner's Applied Linear Models book or Agresti's Categorical Data Analysis, and simpler to grasp than say, Casella's Statistical Inference book or Mccullagh's Generalized Linear Models book. Great book.
0 of 0 people found the following review helpful.
Veryconfusing and difficult to reproduce the examples
By StarDust
Chapter 11 , Discrimination and Classification is , Example 11.3, the author uses Hemophilia dataset. If you go back to the resources the datasets are named in a different order and you cannot identify which is Hemophilia dataset.
The example shows averages by groups for two covariates and these averages do not match the averages in Hemophilia dataset.
y(hat) = (x1(bar) - x2(bar))%*%S(pooled)^-1%*%x, this is very confusing, because what is x1(bar), is it the average of values in x1 covariate ? The notations used are very confusing ?
Also the author uses 0.2418 as value for x1(bar) then after four lines the author uses ( -0.0065 , -0.0390) as values for x1(bar)....ahhh...
Not a good book for "Applied" Multivariate Analysis.
0 of 0 people found the following review helpful.
Great book but BE AWARE OF PAPERBACK VERSION!!
By Amazon Reviewer
Great book. I used it for an online course. It does have a section on matrix algebra and vectors, in case it has been a while since doing matrix algebra. Examples are great and very detailed.
Be aware of paperback version. There is no US paperback version of this! I bought this used for about $80 from seller AlLilydale11 about a year ago. I just tried to resell it and found out I couldn't. The book I have is only worth about $20. I realized that I had paid for a used HARDCOVER US edition of the book, but received a paperback international version. There was too much time in between ordering and receiving that I didn't realize the paperback format was wrong, I just assumed I got what I ordered. This paperback version can only be sold in Asian countries and so if you try to sell it back to a bookstore, they won't take it. The ISBN number cannot be found on a lot of online resell places. Unfortunately, too late to give seller feedback because I am upset.
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