StatisticsSTAT 225 Multivariate Statistical Methods

Introduction to statistical methods for analyzing data sets in which two or more variables play the role of outcome or response. Descriptive methods for multivariate data. Matrix algebra and random vectors. The multivariate normal distribution. Likelihood and Bayesian inferences about multivariate mean vectors. Analysis of covariance structure: principle components, factor analysis. Discriminant, classification and cluster analysis. (Formerly AMS 225.)

Requirements

Prerequisite(s): STAT 206 or STAT 206B, or by permission of instructor. Enrollment is restricted to graduate students.

Credits

5

Quarter offered

Spring

Instructor

The Staff, David Draper, Ju Hee Lee