Course Homepage: Fall 2007

Usually Offered: Alternating Fall semesters

Purpose: To provide graduate students who have training in mathematical statistics with a solid introduction to the theory and implementation of the fundamental methods of multivariate statistics, including procedures such as principal comoponent and factor analysis, canonical correlation analysis, discriminant analysis, multivariate analysis of variance (including repeated measures analysis), cluster analysis, and multidimensional scaling.

Current Textbook: Multivariate Analysis, by K.V. Mardia, J.T. Kent, and J.M. Bibby. Elsevier (Academic Press).

 

Topics Covered	Chapters	Time
Foundations of Multivariate Analysis: Properties of Random Vectors, Theory of The Multivariate Normal Distribution, Multivariate Regression Analysis	1-6	6 weeks
Principal Components and Factor Analysis	8-9	2.5 weeks
Canonical Correlation Analysis	10	1 week
Discriminant Analysis and Multivariate Analysis of Variance	11-12	2.5 weeks
Cluster Analysis and Multidimensional Scaling	13-14	2 weeks

Contact Faculty: Brian Habing
(Last Updated: February 22nd, 2007)