Applied Statistics

Catalog description

This course will examine real data sets from a variety of domains, examine multiple models for these data sets, assess the validity of modeling assumptions, and determine the strength of conclusions that can be drawn. Topics to be covered include: 1) inferential statistics (such as hypothesis testing and estimation in parametric and nonparametric settings, conditional inference, resampling methods, cross-validation, and multiple hypothesis testing); 2) experimental design (analysis of variance) 3) Bayesian statistics (such as prior distributions, posterior and predictive inference, and Bayesian model comparison); 4) Regression and prediction (such as elements of linear and nonparametric regression, assessment of variable importance, introduction to causal inference). The course will include project-based learning and use a statistical programming language such as R or python. A strong emphasis will be placed on the critical analysis of modeling assumptions in real-world settings.