Psychology | Statistical Techniques
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
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
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.