A model-based approach to modern methods in data analysis. Comparison of statistical approaches including frequentist, likelihood, and Bayesian techniques. Methods for simulating data, estimating parameters for single variable models, constructing multivariate and hierarchical models, model comparison using information criteria, and model suitability using posterior predictive analysis.
4
UnitsOptional
Grading1, 2, 3
PasstimeGraduate students only
Level LimitLetters and science
College