For more than four decades An Introduction to Multivariate Statistical Analysis has been an invaluable text for students and a resource for professionals wishing to acquire a basic knowledge of multivariate statistical analysis. Since the previous edition, the field has grown significantly. This updated and improved Third Edition familiarizes readers with these new advances, elucidating several aspects that are particularly relevant to methodology and comprehension.

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.