*****COURSES ARE SUBJECT TO CHANGE*****
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. Prerequisite(s): Either course 206 or 206B or permission by the instructor. Enrollment restricted to graduate students. D. Draper
While the information on this web site is usually the most up to date, in the event of a discrepancy please contact your adviser to confirm which information is correct.