Customer Reviews

Applied Multivariate Statistical Analysis (5th Edition)

5 star:		(12)
4 star:		(2)
3 star:		(4)
2 star:		(5)
1 star:		(5)

 

 

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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.

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.

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.

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.

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

This is a very good, very understandable book. It could (maybe should) be used as a first course in multivariate techniques. Concepts very well explained, it needs no more than a good basic course in statistics to be fully absorbed. It also includes enough matrix algebra to be self-contained.

I'm a graduate student of computer science. This is my third course of probability and statistics and I feel quite comfortable with the main themes in the area. The first two courses were: Introduction to Probability and Statistics (in undergrad) and Advanced Probability for Statistical Inference. (in grad)

First of all, this book is definitely for graduate students. You are assumed to have some mathematical maturity in statistics and linear algebra. But, don't get it wrong. The book doesn't have a rigorous and well established mathematical treatment of the topics. It "sometimes" prove things. Though, it will be painful to keep track of what's going on even under these circumstances, if you are not comfortable let's say with matrix representations (these are actually generalizations of one variable case) of known formulas in probability and statistics.

If you want to use this book as a reference, it's allright. A lot of major topics are well covered with examples. By the way, I disagree with the ones claiming that this is not an application book. It indeed is. Each concept is followed by an example and there's a fair amount of good exercies at the end of each chapter.

But, here's my complaint. It took me a lot of time to understand the "essence", the mathematical idea behind the principal component analysis and factor analysis, for instance. The relationship between these techniques and spectral decomposition, eigenvalues and vectors are not clear when you first read the book. It takes quite a while to digest the material. This is why I consider its treatment as ill-structured. It discusses topics with a rush and a little effort to convey the main ideas. Constructing them as a whole is most of the time left to the reader, which is time consuming. This, I think explains the situation of the reviewers that give 1 star to this book. I agree with them in this sense.

Anyway, if you want to equip yourself with necessary skills to interpret multivariate data, the book is allright at best. But, if you already know the subject or you really want to know this field by heart and experienced in statistics, I'm sure there exists better books.

While this text covers a variety of multivariate techniques, the term "applied" is used loosely, in my opinion.

This is more a math-stat textbook than an applied statistics text.

I wish I had read the reviews (if they existed when I purchased the 4th edition), for I would have purchased a different text.

This book is primarily a theortical text that works in application. If your looking for an "applied" interpretation of Multivariate topics...KEEP LOOKING! This book is intended for statisticians and is complex even for graduate students. Read the matrix algebra sections first, and have a good software package available other than SAS. The SAS code for these applications are not straightforward. If the title were changed to downplay the application, I would give the book 5 stars because the theory is well written.

The book is rigorous enough for grad level stats classes but explains enough that things you haven't seen in a few years are noted again for you. Good text.