Applied Multivariate Data Analysis
Multivariate analysis targets data with simultaneous measurements on many variables and studies the relationship between these variables. This course introduces important multivariate analysis methods used in public health research. Topics include multivariate regression analysis, multivariate analysis of variance (MANOVA), principal component analysis (PCA), factor analysis, discriminant analysis (classification), clustering analysis, canonical correlation analysis (CCA) and correspondence analysis (CA).
Topics
- multivariate regression analysis
- multivariate analysis of variance (MANOVA)
- principal component analysis (PCA)
- factor analysis
- discriminant analysis (classification)
- clustering analysis
- canonical correlation analysis (CCA) and correspondence analysis (CA)
Topics explicitly listed in official catalogue description
Prerequisites & course information
Prerequisite: Must have completed EPIB651 or permission of instructor. Recommended: Previous experience with at least one statistical software package (e.g. SAS, R, STATA). SAS is the main software package used for demonstration in class.
Sources
- Course catalogue ↗2026-2027