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.

Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SAS® Software, Second Edition provides a unique approach to this topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information on

• mixed effects models
• applications of the MIXED procedure
• regression diagnostics with the correspoding IML procedure code
• covariance structures.

The authors' approach to the information aids professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding output accompany sample problems, and clear explanations of the various SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples. High-resolution graphs have been used in this new edition.