An introduction to the concepts, theory, and application of statistical inference, including the structure of statistical problems, probability modeling, data analysis and statistical computing, and linear regression. Inference from the viewpoint of Bayesian statistics, with some discussion of sampling theory methods and comparative inference. Applications to problems in various fields. Prerequisite: (Mathematics 202D, 212, 219, or 222) and (Statistical Science 230, Statistical Science 240L, or Mathematics 340). Instructor: Staff
Prerequisites
Prerequisite: (Mathematics 202D, 212, 219, or 222) and (Statistical Science 230, Statistical Science 240L, or Mathematics 340)