I. GETTING STARTED
1. Aspects of Multivariate Analysis.
2. Sample Geometry and Random Sampling.
3. Matrix Algebra and Random Vectors.
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.
