Multivariate Statistics

Undergraduate

Introduction to the applied analysis of multivariate data. Topics include principal components analysis, factor analysis, cluster analysis, discriminant analysis, multidimensional scaling, multivariate generalized linear models, and structural equations modeling. Non-parametric analogues of many of these topics are also discussed. Many of these techniques are used in machine learning. Extensive computer work using R, SAS or SPSS (examples are provided in all programs). Prerequisites: knowledge of basic inferential procedures and experience with linear models. QR TTh 1:05pm-2:20pm