K310 | 3447 | J. Busemeyer

Statistical inference under uncertainty Prerequisite: Math M119 or equivalent This course provides an introduction to statistical reasoning and inference under uncertainty from a Bayesian point of view. Basic statistics are introduced first, including measures of central tendency, variability, and correlation between variables. These statistics are used to develop simple models for prediction and forecasting based on linear regression. Next the famous Bayes theorem is presented as the rational approach to reasoning under uncertainty. Finally, Bayesian inference methods for hypothesis testing are presented, and Bayesian statistical decision making methods are discussed. Credit given for only one of the following: K300, K310; Criminal Justice P291; Economics E270; Sociology S250; or SPEA K300. Format: Lecture integrated with computer exercises, class notes available on the web. Homework: There will be weekly assignments, some using the computer package, SPSS. Tests: Three examinations. Grading: The final grade is based on total points over the three exams, homework, and class performance. Availability of Instructor: There will be regular office hours, other times by appointment. A graduate assistant will also be available for office hours and by appointment. "Drop-ins" and communication by e-mail are encouraged.