Applied Multivariate Statistical Analysis
Description
For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics.
Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analysing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analysing multivariate data, the text assumes two or more statistics courses as a prerequisite.
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Persons
Dean W. Wichern is Professor Emeritus at the Mays School of Business at Texas A&M University. He holds membership in the American Statistical Association, Royal Statistical Society, International Institute of Forecasters, and Institute for Operations Research and the Management Sciences. He is the author for four textbooks and was Associate Editor of Journal of Business and Economic Statistics from 1983-1991.
Professor Richard A. Johnson is Professor in the Department of Statistics at the University of Wisconsin. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association and he is amember of the Royal Statistical Society and International Statistical Institute. He is the author of six textbooks and over 120 technical publications and is the founding Editor of Statistics and Probability Letters (1981-).
Content
- 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.
- Appendix.
- Data Index.
- Subject Index.
