Statistical Analysis

Description from DukeHub

This course extends CRP 241 (Introduction to Statistical Methods) and primarily considers statistical models with a single predictor, to models containing multiple predictors. We cover models with continuous outcomes (regression, analysis of variance, analysis of covariance), dichotomous outcomes (logistic regression), time to event outcomes (survival models), and count outcomes (Poisson and negative binomial models). Through class lectures, in class demonstrations, directed in class exercises, and discussion of representative research reports from peer-reviewed journals, students are introduced to the core concepts in statistical modeling. Prerequisite: CRP 241. Credit: 4.

Skills

• data and statisticsdescription: "statistical" and 5 more

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• 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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