Skip to main content
Skip to main menu


STAT 4250/6250

Applied Multivariate Analysis and Statistical Learning
Credit Hours:
3 hours

The methodology of multivariate statistics and machine learning for students specializing in statistics. Topics include inference on multivariate means, multivariate analysis of variance, principal component analysis, linear discriminant analysis, factor analysis, linear discrimination, classification trees, multi-dimensional scaling, canonical correlation analysis, clustering, support vector machines, and ensemble methods.

Undergraduate Prerequisite: STAT 4230/6230 and (MATH 3000 or MATH 3300)
Graduate Prerequisite: STAT 6420 or STAT 6220 or STAT 6315 or permission of department

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar givenĀ has a direct impact upon ourĀ students and faculty.