Regression Analysis III: Advanced Methods

Instructor(s):

• David Armstrong, Political Science, University of Oxford

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 causal inference, missing data, and model selection (including multi-model inference). The course assumes an intimate familiarity with the details of OLS regression and a working knowledge of matrices and linear algebra (taking Math for the Social Sciences concurrently should be sufficient in the event of no prior knowledge). The course relies heavily on the use of the R/S computing language. It is imperative that the student either 1) have a working knowledge of R including basic programming or 2) be enrolled in the Summer Program lectures, Introduction to the R Statistical Computing Environment.

Dates:  June 22-July 17 

Fee:  consult the fee structure