Regression Analysis III: Advanced Methods
- David Armstrong, University of Wisconsin at Milwaukee
Linear regression is the workhorse of social science methodology. Its relative robustness and easy interpretation are but two of the reasons that it is generally the first and frequently the last stop on the way to characterizing empirical relationships among observed variables. This course will extend the basic linear model framework in a number of directions in an attempt to fix potential problems in the analysis before they arise. The course takes a modern, data-analytic approach to regression emphasizing graphical tools to aid interpretation and presentation of results. The course moves through both low- and high-tech diagnostics for and solutions to common obstacles for estimation of linear models, namely non-linearity, outlying observations, and dependent data. We will also spend some time talking about model selection (including multi-model inference), finite mixture models and, if time allows, missing data and multiple imputation. The course assumes an intimate familiarity with the details of OLS regression and a working knowledge of matrices and linear algebra (taking Mathematics for Social Scientists II lecture concurrently should be sufficient in the event of no prior knowledge). In the past, the course has relied exclusively on the R computing language. This year, for the first time, the course will also provide code and support for Stata as well, where applicable. For those wishing to learn R, this course would be a great place to do it in conjunction with the Summer Program lectures -- Introduction to the R Statistical Computing Environment.
Fees: Consult the fee structure.
Location: ICPSR -- Ann Arbor, MI
Date(s): June 24 - July 19
Time: 3:00 PM - 5:00 PM