A range of useful linear models will be studied, including multiple regression, non-linear regression, and hierarchical random effects models. Models will be specified and fit following the Bayesian paradigm with inference based on Markov chain Monte Carlo (MCMC) samples from posterior distributions. Particular attention will be paid to model formulation, computing, diagnostics, and summary for scientific publications, all within the R statistical environment. Topics will be motivated using example data and participants are encouraged to actively work through the examples. The workshop is geared toward advanced undergraduates and graduate students who have some familiarity with applied regression analysis and R.