Applied Regression Analysis (STAT*3240)
Code and section: STAT*3240*01
Term: Fall
Details
This course reviews simple linear regression and introduces multiple regression with emphasis on theory of least squares estimation, residual analysis, and model interpretation. Within the multiple regression context, transformations of variables, interactions, model selection techniques, ANOVA, influence diagnostics and multicollinearity will be discussed. Topics may also include Box-Cox transformations, weighted regression, and logistic and Poisson regression. This course is supplemented with computer labs involving interactive data analysis using statistical software.
Syllabus
| Attachment | Size |
|---|---|
 Course Outline (Fall 2015) | 267.09 KB |
| Course Outline (Fall 2016) | 121.87 KB |
| Course Outline (Fall 2017) | 148.76 KB |
| Course Outline (Fall 2018) | 298.67 KB |
| Course Outline (Fall 2019) | 511.51 KB |
| Course Outline (Fall 2020) | 140.71 KB |
| Course Outline (Fall 2021) | 132.32 KB |
| Course Outline (Fall 2022) | 131.42 KB |
| Course Outline (Fall 2023) | 140.63 KB |