Applied Multivariate Data Analysis

This course examines the principles behind statistical data analysis for multivariate data, and introduces the students to major areas of multivariate I data analysis. Topics include multiple and logistic regression, principal component analysis, factor analysis, cluster analysis, MANOVA, multidimensional scaling, discriminant analysis and canonical correlation. The students will use at least one statistical software package.

Prerequisites: Graduate standing in M.S. in Data Analytics or (MATH 01131 and MATH 01210) and (STAT 02360 and STAT 02260 or STAT 02290) or permission of the instructor