Applied Multivariate Statistical Analysis (Classic Version)

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-).

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.