STATS 513
Regression and Data Analysis
The course is designed for (non-statistics) graduate students interested in quantitative research to learn about regression models for data analysis. Topics include estimation and inference, diagnostics, model selection, and interpretation of results associated with linear models and general linear models. Additional topics may vary with the instructor.
Topics
- estimation and inference
- diagnostics
- model selection
- and interpretation of results associated with linear models and general linear models
Topics explicitly listed in official catalogue description
Prerequisites & course information
No credit in STATS 500. (Prerequisites enforced at registration.) Linear Algebra (at the level of MATH 214 or equivalent) AND Statistics (at the level of STATS 412 or STATS 426). May not be repeated for credit.
Sources
- Course catalogue ↗2026-2027