Time Schedule:
Michael D. Ward
POL S 503
Seattle Campus
Theory and practice of likelihood inference. Includes probability modeling, maximum likelihood estimation, models for binary responses, count models, sample selection, and basis time series analysis. Offered: jointly with CS&SS 503.
Class description
Description: Estimation of maximum likelihood estimation of social science models, spanning binary, count, and continuous random variables. We will also cover spatial analysis and panel regression. Presentation and interpretation of results from the estimations of these models will also be emphasized.
Student learning goals
General method of instruction
There will be a weekly lecture. Most weeks will involve a homework assignment in which students will undertake analysis of a database from a published article in the social sciences. We will develop the necessary code and explore the results in class.
Recommended preparation
Text: Fox, Applied Regression Analysis Linear Model; Fox, R & S Plus Companion to Applied Regression.
Students should have an introductory statistics course at the level of POL S 501, or higher.
Class assignments and grading
Class Assignments and Grading: There are two basic sets of assignments. The first consists of weekly assignments in which replications of published articles using maximum likelihood methods are employed. The second is a project in which a new database, of the student's choosing, is analyzed and interpreted, with an eye toward eventual publication, About 50/50 on weekly work and end of term project.
About 50/50 on weekly work and end of term project.
