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STAT 3215Q: Applied Linear Regression in Data Science
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.
- Credits
- 3
- Offered
- Spring 2027, Fall 2026
- Campuses
- Storrs
- Gen Ed
- Q: Quantitative Competency
- Requirements
- STAT 2215Q or 3025Q or instructor consent. Not open for credit to students who have passed STAT 3115Q or 5315.
Spring 2027 sections
Taught by Elizabeth Schifano.
| Section | Type | Meets | Instructor | Campus | Room | Seats |
|---|---|---|---|---|---|---|
| 001 | LEC | 2:00PM - 3:15PM / TuTh | Elizabeth Schifano | Storrs | AUST 247 | 18 of 39 open |
Seats as of Nov 12, 2026. HuskyScheduler shows live seats when you search.
Fall 2026 sections
Taught by Elizabeth Schifano and Uladzimir Charniauski.
| Section | Type | Meets | Instructor | Campus | Room | Seats |
|---|---|---|---|---|---|---|
| 001 | LEC | 12:30PM - 1:45PM / MoWe | Elizabeth Schifano, Uladzimir Charniauski | Storrs | AUST 313 | 15 of 40 open |
Seats as of Nov 12, 2026. HuskyScheduler shows live seats when you search.