Applied Multivariate Statistical Analysis (Classic Version), 6th edition

Published by Pearson (March 18, 2018) © 2019

  • Richard A. Johnson University of Wisconsin-Madison
  • Dean W. Wichern Texas A&M University, College Station
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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 analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.

  • I. GETTING STARTED.
  • 1. Aspects of Multivariate Analysis.
  • 2. Matrix Algebra and Random Vectors.
  • 3. Sample Geometry and Random Sampling.
  • 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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