REGRESSION METHODS FOR POPULATION HEALTH

Prerequisites & course information

Requisites: STAT/B M I 541 or POP HLTH/B M I 551 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. State the assumptions underlying linear, logistic, multinomial and Poisson regression models, recognize and address violations of those assumptions, and estimate and interpret regression models to answer epidemiologic and public health research questions. Audience: Graduate 2. Critique uses of linear, logistic, multinomial and Poisson regression models in the epidemiologic and public health literature. Audience: Graduate 3. Translate epidemiologic concepts into statistical modeling assumptions, and explain statistical modeling assumptions in epidemiologic terms. Audience: Graduate 4. Recognize applications that require methods beyond their expertise, and identify resources to learn about more advanced techniques. Audience: Graduate Learning Outcomes: 1. State the assumptions underlying linear, logistic, multinomial and Poisson regression models, recognize and address violations of those assumptions, and estimate and interpret regression models to answer epidemiologic and public health research questions. Audience: Graduate 2. Critique uses of linear, logistic, multinomial and Poisson regression models in the epidemiologic and public health literature. Audience: Graduate 3. Translate epidemiologic concepts into statistical modeling assumptions, and explain statistical modeling assumptions in epidemiologic terms. Audience: Graduate 4. Recognize applications that require methods beyond their expertise, and identify resources to learn about more advanced techniques. Audience: Graduate