Includes bibliographical references and indexes.

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.

An updated edition, this book provides explanations of the results needed to understand output from the standard multivariate analysis computer packages and prepares readers to make proper interpretations, select appropriate techniques and understand their strengths and weaknesses