Multivariate data commonly encountered in a variety of disciplines is easy to understand with the approaches and methods described in this book. The conceptual developments, theory, methods, and subsequent data analyses are presented systematically and in an integrated manner. The data analysis is performed using many multivariate analysis components available in SAS software. Illustrations are provided using an ample number of real data sets drawn from a variety of fields, and special care is taken to explain the SAS codes and the interpretation of corresponding outputs. As a companion volume to the authors' previous book, Applied Multivariate Analysis with SAS Software, 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. Since the techniques discussed in this book also form the foundation of data mining methodology, the book will be of interest to data mining practitioners.Statistics and the Law DODGE ai Alternative Methods of Regression DOWDY and WEARDEN . Statistics for Research ... Multivariate Statistical Simulation JOHNSON and KOTZ . Distributions in ... Solutions Manual to Accompany Loss Models: From Data to Decisions KOTZ, BALAKRISHNAN, and JOHNSON . Continuousanbsp;...
|Title||:||Multivariate Data Reduction and Discrimination with SAS Software|
|Author||:||Ravindra Khattree, Dayanand N. Naik|
|Publisher||:||SAS Institute - 2000-05-01|