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This classroom-tested text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the necessary knowledge to make proper interpretations and select appropriate techniques for analyzing multivariate data. It is suitable for courses in Multivariate Statistics, Marketing Research, Statistics in Education and postgraduate-level courses in Experimental Design and Statistics. Key Features Accessible level: Presents the concepts and methods of multivariate analysis at a level that is readily understandable by readers who have taken two or more statistics courses. Organization and approach: Contains the methodological “tools” of multivariate analysis. An abundance of examples and exercises based on real data Includes, in some cases, snapshots of the corresponding SAS output. Emphasis on applications of multivariate methods. A clear and insightful explanation of multivariate techniques. About the Author Richard A. Johnson is Professor in the Department of Statistics at the University of Wisconsin. Dean W. Wichern is Professor Emeritus at the Mays School of Business at Texas A&M University. Table of Contents Preface 1. Aspects of Multivariate Analysis 2. Matrix Algebra and Random Vectors 3. Sample Geometry and Random Sampling 4. The Multivariate Normal Distribution 5. Inferences about a Mean Vector 6. Comparisons of Several Multivariate Means 7. Multivariate Linear Regression Models 8. Principal Components 9. Factor Analysis and Inferences for Structured Covariance Matrices 10. Canonical Correlation Analysis 11. Discrimination and Classification 12. Clustering, Distance Methods, and Ordination Appendix Data Index Subject Index