Covers linear models, generalized linear and non-linear regression, and models. Includes interpretation of parameters, including collapsibility and non-collapsibility, estimating equations; likelihood; sandwich estimations; the bootstrap; Bayesian inference: prior specification, hypothesis testing, and computation; comparison of approaches; and diagnostics. Prerequisites: STAT 512 and STAT 513; either BIOST 533/STAT 533, or STAT 502 and STAT 504/CS&SS 504; and a course in matrix algebra. Offered: jointly with STAT 570; Autumn