Applied Multivariate Statistical Analysis

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DRAFT (NOTE: *Each chapter begins with an Introduction, and
concludes with Exercises and References.*) I. GETTING STARTED.
1. Aspects of Multivariate Analysis.

Applications of Multivariate Techniques. The Organization of Data.
Data Displays and Pictorial Representations. Distance. Final
Comments.

2. Sample Geometry and Random Sampling.

The Geometry of the Sample. Random Samples and the Expected
Values of the Sample Mean and Covariance Matrix. Generalized
Variance. Sample Mean, Covariance, and Correlation as Matrix
Operations. Sample Values of Linear Combinations of Variables.3.
Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector
Algebra. Positive Definite Matrices. A Square-Root Matrix. Random
Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix
Inequalities and Maximization. Supplement 2A Vectors and Matrices:
Basic Concepts. 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.**

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