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This solution ( solutions ) manual was prepared as an aid for instructors who will benefit by having solutions available. In addition to providing detailed answers to most of the problems in the book, this manual can help the instructor determine which of the problems are most appropriate for the class. The vast majority of the problems have been solved with the help of available the problems have been solved with software (SAS, S~Plus, Minitab). A few of computer hand calculators. The reader should keep in mind that round-off errors can occurparcularly in those problems involving long chains of arithmetic calculations.
We would like to take this opportunity to acknowledge the contribution of many students, whose homework formed the basis for many of the solutions. In paricular, we would like to thank Jorge Achcar, Sebastiao Amorim, W. K. Cheang, S. S. Cho, S. G. Chow, Charles Fleming, Stu Janis, Richard Jones, Tim Kramer, Dennis Murphy, Rich Raubertas, David Steinberg, T. J. Tien, Steve Verrill, Paul Whitney and Mike Wincek. Dianne Hall compiled most of the material needed to make this current solutions manual consistent with the sixth edition of the book. The solutions are numbered in the same manner as the exercises in the book. Thus, for example, 9.6 refers to the 6th exercise of chapter 9. We hope this manual is a useful aid for adopters of our Applied Multivariate Statistical Analysis, 6th edition, text. The authors have taken a little more active role in the preparation of the current solutions manual. However, it is inevitable that an error or two has slipped through so please bring remaining errors to our attention. Also, comments and suggestions are always welcome.
Richard A. Johnson Dean W. Wichern
Download free Solution ( solutions ) Manual For Applied Multivariate Statistical Analysis (Classic Version), 6th Edition By Johnson
Table of Contents
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 AnalysisIV. CLASSIFICATION AND GROUPING TECHNIQUES.
11. Discrimination and Classification.
12. Clustering, Distance Methods and Ordination.
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