3 credits
Fall 2026 Lecture Upper Division
Extension of univariate tests in normal populations to the multivariate case, equality of covariance matrices, multivariate analysis of variance, discriminant analysis and misclassification errors, canonical correlation, principal components, factor analysis. Strong emphasis will be placed on use of existing computer programs.
Learning Outcomes1Describe the underlying mathematical formulation associated with multivariate techniques such as principal components, factor analysis, discrimination and classification, and clustering.
2Derive properties associated with the multivariate normal probability distribution.
3Analyze data using multivariate techniques such as principal components, factor analysis, discrimination and classification, and clustering.
4Use appropriate software, such as R, to describe, visualize, and analyze multivariate data.
5Communicate the results of the work to a variety of audiences, including a non-statistician.
Prerequisites
Restrictions
made by Purdue students.