Introduction to probability and statistical inference. Examine events and random experiments; basic rules of probability; joint, conditional, and marginal probability and independence; discrete and continuous random variables; random sampling and estimation; large-sample theory results and central limit theorem-based inferential summaries; and maximum likelihood estimation. Three lecture hours and two laboratory hours a week for one semester. Statistics and Data Sciences 321 and 431 may not both be counted. Prerequisite: Statistics and Data Sciences 315 with a grade of at least C-; and credit with a grade of at least C- or registration for Mathematics 408D, 408L, or 408S.