Methods of Multivariate Analysis (eBook)

ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Linear Models in Statistics and Multivariate Statistical Inference and Applications, both available from Wiley.

Introduction.

Matrix Algebra.

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.

Multivariate Regression.

Canonical Correlation.

Principal Component Analysis.

Factor Analysis.

Cluster Analysis.

Graphical Procedures.

Tables.

Answers and Hints to Problems.

Data Sets and SAS Files.

References.

Index.

"...a systematic, well-written text...there is much
practical wisdom in this book that is hard to find elsewhere. It
belongs in serious data analysts' libraries..." (IIE
Transactions-Quality and Reliability Engineering, November
2005)

"...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)