Publisher: Prentice Hall; 4 edition Authors: Richard A. Johnson, Dean W. Wichern.
本书是多元统计分析的经典，远比国内其他教材质量高，另外还配有讲义，是非常难得的好资料！！！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.
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.