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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.