Introduction to Multivariate Statistics
** available as of 06/15/2026
** available as of 06/15/2026
Prerequisites: STAT 308.
This course introduces students to the concepts and methods used to analyze multivariate data in an applied context. Topics include exploring and visualizing multivariate data, linear algebra for multivariate methods, principal components and factor analysis, canonical correlation, discriminant and cluster analysis, multivariate normal distributions, MANOVA, and other dimensionality reduction techniques. Emphasis will be placed on interpreting results and applying methods using real-world data. Statistical software such as R will be used for computation and visualization.
Outcomes: Students will be able to: 1) describe and visualize relationships among multiple variables in multivariate datasets; 2) apply linear algebra concepts relevant to multivariate analysis; 3) perform and interpret principal component and factor analyses; 4) communicate results from multivariate analyses clearly and effectively.
This course introduces students to the concepts and methods used to analyze multivariate data in an applied context. Topics include exploring and visualizing multivariate data, linear algebra for multivariate methods, principal components and factor analysis, canonical correlation, discriminant and cluster analysis, multivariate normal distributions, MANOVA, and other dimensionality reduction techniques. Emphasis will be placed on interpreting results and applying methods using real-world data. Statistical software such as R will be used for computation and visualization.
Outcomes: Students will be able to: 1) describe and visualize relationships among multiple variables in multivariate datasets; 2) apply linear algebra concepts relevant to multivariate analysis; 3) perform and interpret principal component and factor analyses; 4) communicate results from multivariate analyses clearly and effectively.