Characterizing and Displaying Multivariate Data.
The Multivariate Normal Distribution.
Tests on One or Two Mean Vectors.
Multivariate Analysis of Variance.
Tests on Covariance Matrices.
Discriminant Analysis: Description of Group Separation.
Classification Analysis: Allocation of Observations to Groups.
Principal Component Analysis.
Answers and Hints to Problems.
Data Sets and SAS Files.
"...extends univariate procedures...to analogous multivariate techniques involving several dependent variables..." (SciTech Book News, Vol. 26, No. 2, June 2002)
"...a practitioner who wants to carry out multivariate techniques in applied work and to interpret the results must have this book..." (Technometrics, Vol. 45, No. 1, February 2003)
"...I have not found a better text for a masters-level class in multivariate methods." (Journal of the American Statistical Association, March 2003)
"This book strikes a nice balance between meeting the needs of statistics majors and students in other fields. The discussion of each multivariate technique is straightforward and quite comprehensive. This textbook is likely to become a useful reference for students in their future work." (Journal of the American Statistical Association)
"In this well-written and interesting book, Rencher has done a great job in presenting intuitive and innovative explanations of some of the otherwise difficult concepts." (CHOICE)
"This book is excellent for an introductory course in multivariate analysis for students with minimal background in mathematics and statistics." (Technometrics)
"Excellent introduction to standard topics in multivariate analysis." (American Mathematical Monthly)