Multivariate Statistical Methods: Advanced Topics


  • Robert Henson, University of North Carolina at Greensboro

The purpose of this workshop is to discuss multivariate distributions and the role they play in modern methods for analyzing multivariate data. The course is designed to be an introduction to multivariate thinking, covering concepts that underlie many statistical models in widespread use today. The course will begin with a review of univariate linear models and an introduction to matrix algebra. Following that, the course will introduce the multivariate normal distribution and demonstrate its properties by covering classical multivariate methods such as MANOVA, discriminant analysis, principal components analysis, and factor analysis. The course will conclude by discussing classical multivariate techniques such as cluster analysis, multidimensional scaling, and correspondence analysis.

A very strong background in statistics, at least at the level of the ICPSR courses Regression Analysis II: Linear Models and Mathematics for Social Scientists, II, is necessary for this course. The level and breadth of coverage is roughly equivalent to that found in the following multivariate texts: Cooley and Lohnes, Multivariate Data Analysis; Tatzuoka, Multivariate Analysis; and Johnson and Wichern, Applied Multivariate Statistical Analysis.

Fees: Consult the fee structure.

Tags: multivariate statistics, factor analysis, principal components analysis, cluster analysis, canonical correlation, multidimensional scaling, MANOVA, discriminant analysis

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 24 - July 19

Time: 3:00 PM - 5:00 PM


  • Robert Henson, University of North Carolina at Greensboro