Statistical Modeling I
An introduction to core applied statistical modeling ideas from a probabilistic, Bayesian perspective. Topics include exploratory data analysis, programming in R, Bayesian probability models, an introduction to the Gibbs sampler, applied regression analysis, and hierarchical models. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C. Three lecture hours a week for one semester.
Topics
- exploratory data analysis
- programming in R
- Bayesian probability models
- an introduction to the Gibbs sampler
- applied regression analysis
- and hierarchical models
Topics explicitly listed in official catalogue description
Prerequisites & course information
An introduction to core applied statistical modeling ideas from a probabilistic, Bayesian perspective. Topics include exploratory data analysis, programming in R, Bayesian probability models, an introduction to the Gibbs sampler, applied regression analysis, and hierarchical models. Only one of the following may be counted: Statistics and Data Sciences 391P (Topic 1), Statistics and Data Sciences 383C, Statistics and Scientific Computation 383C. Three lecture hours a week for one semester.
Sources
- Course catalogue ↗2026-2027