Intermediate Statistical and Computational Methods

This intermediate statistics course is intended to give students familiarity with statistical tools used to analyze data in a variety of disciplines, including psychology, and provides experience reading and understanding studies based on data analysis. Topics include linear and polynomial regression, logistic regression, Bayes theorem and linear discriminant analysis; cross-validation and the bootstrap, permutation tests, MCMC, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Bayesian inference; Bayesian networks; mixture models; some unsupervised learning and topics in R.