An Introduction to Multivariate Statistical Analysis, 3e
T. W. Anderson (Stanford Univ., CA)



Preface to the Third Edition. Preface to the Second Edition.
Preface to the First Edition.
1. Introduction.
2. The Multivariate Normal Distribution.
3. Estimation of the Mean Vector and the Covariance Matrix.
4. The Distributions and Uses of Sample Correlation Coefficients.
5. The Generalized T2-Statistic.
6. Classification of Observations.
7. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance.
8. Testing the General Linear Hypothesis: Multivariate Analysis of Variance
9. Testing Independence of Sets of Variates.
10. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices.
11. Principal Components.
12. Cononical Correlations and Cononical Variables.
13. The Distributions of Characteristic Roots and Vectors.
14. Factor Analysis.
15. Pattern of Dependence; Graphical Models.
Appendix A: Matrix Theory.
Appendix B: Tables.
References.
Index.







Applied Multivariate Statistical Analysis  6ePublisher: Prentice Hall;  
Authors: Richard A. Johnson, Dean W. Wichern

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.