Applied Multivariate Analysis

3 Credits · Level: Graduate, Undergraduate

This course is an introduction to the analysis of multivariate data. Topics include matrix algebra and random vectors, the multivariate normal distribution, Hotelling's T^2, multivariate linear regression models, principal components, factor analysis, and discriminant analysis. The overarching learning goal for this course is for students to acquire an understanding and ability to analyze multivariate data using the software R. Students will (both in and outside of class) complete analytical and computational assignments, culminating in the completion of an applied multivariate analysis project and presentation.