STAT 437 - Introduction to Applied Multivariate Analysis

4 Credit(s)

This course serves as an applied workshop in which students will learn both exploratory and inferential statistical techniques to analyze multivariate datasets. Standard techniques of multivariate analysis will be covered, such as principal components analysis, discriminant and canonical variates analysis, multidimensional scaling, principal coordinates analysis, cluster analysis, multiple regression, canonical correlation analysis, factor analysis, path analysis and Mantel’s test. Both parametric and non-parametric approaches to statistical inference will be covered. In most cases, this course will be themed so that real data sets are taken from a specific application area (such as ecology, genetics, geology, information science, etc.) and are analyzed and/or modeled using appropriate techniques. In light of this, the techniques students use in these fields will vary from class to class.

Prerequisite(s): STAT 217 (or STAT 233) and M 210 grade B- or higher; or c/i.
(fall/ odd-numbered year).