Principles of Multivariate Analysis A User's Perspective [Paperback]

This book is an introduction to the principles and methodology of modern multivariate statistical analysis. It's emphasis is problem-oriented and stresses geometrical intuition in preference to algebraic manipulation. Mathematical sections that are not essential for practical understanding of the techniques are clearly indicated so that they may be skipped if necessary. Discrete and mixed variable techniques are presented as well as continuous variable techniques. New additions to this update include references, a survey of the most recent developments and a new appendix which traces developments that have taken place in the years since publication of the first edition.

Introduction
Part I: Looking at multivariate data
1. Motivation and fundamental concepts
2. One-way graphical representation of data matrices
3. Graphical methods for association or proximity matrices
4. Two-way graphical representation of data matrices
5. Analytical comparison of two or more graphical representations
Part II: Samples, populations, and models
6. Data inspection or data analysis?
7. Distribution theory
Part III: Analysing ungrouped data
8. Estimation and hypothesis testing
9. Reduction of dimensionality: inferential aspects of descriptive methods
10. Discrete data
Part IV: Analysing grouped data
11. Incorporating group structure: descriptive statistics
12. Inferential aspects: the two-group case
13. Inferential aspects: more than two groups
Part V: Analysing association among variables
14. Measuring and interpreting association
15. Exploiting observed associations: manifest-variable models
16. Explaining observed associations: latent-variable models
17. Conclusion: some general multivariate problems
Appendix A--Some basic matrix theory
Appendix B--Postscript: further developments
References
Additional references for lC–