Applied Multivariate Statistical Analysis

Part 1 Getting started: aspects of multivariate analysis; matrix algebra and random vectors; sample geometry and random sampling; the multivariate normal distribution. Part 2 Inferences about multivariate means and linear models: inferences about a mean vector; comparisons of several multivariate means; multivariate linear regression models. Part 3 Analysis of covariance structure: principal components; factor analysis and inference for structured covariance analysis; canonical correlation analysis. Part 4 Classification and grouping techniques: discrimination and classification; clustering.