Regression Analysis: Theory and Applications

Description from DukeHub

In depth study of regression modeling with emphasis on applications and theoretical underpinnings. Linear and logistic regression, analysis of variance, model diagnostics, and selection. Maximum likelihood and least squares estimation of regression parameters. Examples from a variety of fields. Prerequisite: Either any STA 100-level course or STA 230, 231, or 240L and MATH 216, 218, or 221. Recommended co-requisite: STA 230, 231, or 240L. Interested students with different backgrounds should seek instructor consent. This course is required for Statistical Science majors and students interested in taking STA 360. Students interested in an applied-only regression course should take STA 210.

Skills

• data and statisticsdescription: "regression" and 6 more
• lab workhas a lab component

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "diagnostics"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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