Principles of Multivariate Analysis is an introduction to the principles and methodology of modern multivariate statistical analysis. It is written for the user and potential user of multivariate techniques as well as for students coming to the subject for the first time. The author's emphasis is problem-orientated and he is at pains to stress geometrical intuition in preference to algebraic manipulation. Mathematical sections that are not essential for a practical understanding of the techniques are clearly indicated so that they may be skipped by the non-specialist. Discrete and mixed variable techniques are presented as well as continuous variable techniques to give a comprehensive coverage of the subject.
This updated edition includes a new appendix which traces developments that have taken place in the years since Principles of Multivariate Analysis of the first edition and which clarifies some issues raised by readers of the original text. References to about 60 recent books and articles supplement the material in this appendix.Overall, Principles of Multivariate Analysis provides an up-to-date and readable practical account of the subject, both for students of statistics and for research workers in subjects as diverse as anthropology, education, industry, medicine and taxonomy. The new edition includes a survey of the most recent developments in the subject.
"This is an excellent book [...] Theoretical as well as applied statisticians should keep it in their collection."
- Journal of Statistical Computation and Simulation
" [...] it should serve as an excellent reference for anyone interested in multivariate methods."
- Journal American Statistical Asociation
Part I: Looking at multivariate data
Part II: Samples, populations, and models
Part III: Analysing ungrouped data
Part IV: Analysing grouped data
Part V: Analysing association among variables
Appendix: some basic matrix theory
A2 Elementary arithmetic operations
A3 Determinants and inverses
A4 Quadratic forms
A5 Latent roots and vectors
A6 Matrix square root
A7 Partitioned matrices
A8 Vector differentiation
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