Multivariate Data Reduction and Discrimination with SAS Software

Easy to read and comprehensive, this book presents descriptive multivariate (DMV) statistical methods using real-world problems and data sets. It offers a unique approach to integrating statistical methods, various kinds of advanced data analyses, and applications of the popular SAS software aids. Emphasis is placed on the correct interpretation of output to draw meaningful conclusions in a variety of disciplines and industries.Übersichtlich und gut verständlich erläutert dieses Buch multivariate statistische Methoden anhand praxisnaher Beispiele und Datensätze. Der Schwerpunkt liegt darauf, den Leser zur sinnvollen Interpretation von Datenmaterial zu befähigen. Viel Raum wird der Diskussion von SAS-Code und dem zugehörigen Output gegeben, wobei die geeigneten SAS-Prozeduren an Beispielproblemen anschaulich erklärt werden.
Basic Concepts for Multivariate Statistics.

Principal Component Analysis.

Canonical Correlation Analysis.

Factor Analysis.

Discriminant Analysis.

Cluster Analysis.

Correspondence Analysis.

Appendix.

References.

Index. "...a core or supplementary text for graduate or senior undergraduate students, and a reference for researchers and practitioners." (SciTech Book News, Vol. 24, No. 4, December 2000)

Ravindra Khattree is an Indian-American statistician and professor of statistics at Oakland University. His contribution to the Fountain-Khattree-Peddada Theorem in Pitman measure of closeness is one of the important results of his work. Khattree is the coauthor of two books and has coedited two volumes. Dayanand N. Naik is the author of Multivariate Data Reduction and Discrimination with SAS Software, published by Wiley. Multivariate data commonly encountered in a variety of disciplines is easy to understand with the approaches and methods described in Multivariate Data Reduction and Discrimination with SAS Software. Conceptual developments, theory, methods, and subsequent data analyses are presented systematically and in an integrated manner. Data analysis is performed using many multivariate analysis components available in SAS software. The book provides illustrations using ample numbers of real data sets drawn from a variety of fields, and special care is taken to explain the SAS programs and corresponding output. As a companion volume to their previous book, Applied Multivariate Statistics with SAS Software, Second Edition, which discusses multivariate normality-based analyses, this book covers topics where, for the most part, assuming multivariate normality (or any other distributional assumption) is not crucial. As the techniques discussed in the this book also form the foundation of data mining methodology, the book will be of interest to data mining practitioners.