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3215Q. Applied Linear Regression in Data Science
3.00 credits
Prerequisites: STAT 2215Q or 3025Q or instructor consent. Not open for credit to students who have passed STAT 3115Q or 5315.
Grading Basis: Graded
Applied multiple linear regression analysis in data science, with an emphasis on modern statistical regression methods: simple linear regression and correlation analysis, multiple linear regression, analysis of variance, goodness of fit, comparing regression models through partial and sequential F tests, dummy variables, regression assumptions and diagnostics, model selection and penalized regression, prediction and model validation, principles of design of experiments, one-way and two-way analysis of variance.
Last Refreshed: 25-APR-24 05.20.27.822686 AM
| Term | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Location | Enrollment | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1243 5893 1 001 | Spring 2024 | Storrs | In Person | Chen, Ming-Hui Seo, Min |
001 | Reg | TuTh 11:00am‑12:15pm |
AUST 163 | 19/40 | |
| 1248 8789 1 001 | Fall 2024 | Storrs | In Person | Schifano, Elizabeth | 001 | Reg | TuTh 11:00am‑12:15pm |
AUST 103 | 18/40 |