Multivariate Data Reduction and Discrimination with SAS Software

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.

Spis treści:
Basic Concepts for Multivariate Statistics.
Principal Component Analysis.
Canonical Correlation Analysis.
Factor Analysis.
Discriminant Analysis.
Cluster Analysis.
Correspondence Analysis.
Appendix.
References.
Index.

Okładka tylna:
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 ou tput. 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.