Regression Analysis III: Advanced Methods

Instructor(s):

  • 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 simple and sophisticated 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). The course relies entirely on the R computing language. In the past, motivated participants have found that this course, taken in conjunction with the Introduction to R lectures, provides a sufficiently broad introduction to R that they can effectively use it not only at the Summer Program, but also as they return to their home departments; the course has generated many R converts. That said, I do have code that will do many (though not all) of the things we do in the course in Stata. The Stata code requires the user to be at least open to, if not familiar with, programming (i.e., loops, macros, etc...) in Stata. I am happy to help those interested users accomplish what is possible in Stata, but a number of things will not be possible.

Fees: Consult the fee structure.

Tags: regression, regression diagnostics, R-statistical computing environment, outliers, goodness of fit, linear regression, model selection

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 23 - July 18

Time: 3:00 PM - 5:00 PM

Instructor(s):

  • David Armstrong, University of Wisconsin at Milwaukee

Syllabus:

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