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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: 27-FEB-24 AM
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Term Campus Instruction Mode Instructor Section Session Schedule Location Enrollment Notes
Spring 2024 Storrs In Person Chen, Ming-Hui
Seo, Min
001 Reg TuTh 11:00am‑12:15pm
AUST 163 19/40