APPLIED MULTIVARIATE ANALYSIS
Multivariate statistical learning methods are essential and broadly applicable tools for analyzing and understanding complex datasets. Emphasizes the conceptual underpinnings, core models, and basic statistical reasoning underlying the techniques. Topics include: dimension reduction (e.g., principal component analysis, multidimensional scaling, factor analysis), clustering (e.g., k-means, hierarchical, density-based, and model-based clustering), and classification (e.g., discriminant analysis, classification tree, and ensemble methods). Utilizes the R programming language.
Prerequisites & course information
Requisites: ( STAT 333 or 340 ) and ( MATH 320 , 340 , 341 , 345 , or 375 ), graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Describe the purpose and basic concepts of multivariate analysis, distinguishing it from other common forms of statistical analysis such as regression analysis. Audience: Undergraduate 2. Define fundamental concepts of multivariate analysis, such as covariance and correlation, and explain their meaning. Audience: Undergraduate 3. Explain common methods of multivariate analysis such as principal components analysis, factor analysis, multidimensional scaling, and clustering; this includes identifying situations in which these methods would be useful, describing how they are performed, and explaining how to interpret their outputs. Audience: Undergraduate 4. Use statistical software such as R to create meaningful visualizations of multivariate data, and to carry out multivariate analyses. Audience: Undergraduate 5. Properly interpret, contextualize, and communicate the results of an applied multivariate analysis. Audience: Undergraduate Learning Outcomes: 1. Describe the purpose and basic concepts of multivariate analysis, distinguishing it from other common forms of statistical analysis such as regression analysis. Audience: Undergraduate 2. Define fundamental concepts of multivariate analysis, such as covariance and correlation, and explain their meaning. Audience: Undergraduate 3. Explain common methods of multivariate analysis such as principal components analysis, factor analysis, multidimensional scaling, and clustering; this includes identifying situations in which these methods would be useful, describing how they are performed, and explaining how to interpret their outputs. Audience: Undergraduate 4. Use statistical software such as R to create meaningful visualizations of multivariate data, and to carry out multivariate analyses. Audience: Undergraduate 5. Properly interpret, contextualize, and communicate the results of an applied multivariate analysis. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027