3 credits
Spring 2026 Lecture Distance Learning Upper Division
As a sequel to STAT 52500, this course introduces statistical modeling tools for situations where standard least-squares techniques may not apply. This includes an extensive coverage of generalized linear models (GLM) for non-Gaussian responses, mixed effects models to describe correlated data, nonparametric regression, and lastly, parametric and nonparametric survival models for the analysis of (possibly censored) time-to-event data. Among issues to be discussed are the estimation of the models, the testing of hypotheses, and the checking of model adequacy. Data examples will be used throughout the course to illustrate the methodologies and the related software tools in R.
Learning Outcomes1Understand the concepts and methods in generalized linear models, survival analysis, linear mixed models and GEE methods.
2Apply these methods properly to real data using R, drawing valid conclusions, and presenting the results concisely.
Prerequisites
Restrictions
made by Purdue students.