1B Introduction to Regression
Brian Schaffner, University of Massachusetts
9 - 20 July (two week course / 35 hrs)
Detailed Course Outline [PDF]
Course Content
The primary focus of the course is the development of the linear regression model. We begin by estimating regression coefficients using Ordinary Least Squares (OLS) and the assumptions underlying this model. This includes a discussion of hypothesis testing and measures of goodness of fit. We also examine the limitations of regression analysis, including violations of the assumptions underlying the OLS model and how to correct for them. This will teach you how to address the most common problems encountered when using regression analysis.
Course Objectives
- Use Stata to generate and present regression output.
- Interpret regression output, including the coefficient estimates, their statistical significance, and summary statistics designed to capture the quality of the regression as a whole.
- Identify (and where possible correct for) weaknesses associated with the regression model.
Course Prerequisites
Participants should have some background knowledge of basic descriptive statistics and be comfortable with linear algebra.
Background Reading
Pollock III, Philip H. 2005. The Essentials of Political Analysis (2nd ed.). Washington, DC: Congressional Quarterly Press.
Pollock III, Philip H. 2006. A Stata Companion to the Essentials of Political Analysis. Washington, DC: Congressional Quarterly Press.