LPO 8851: Regression Analysis I
PhD course, Vanderbilt University, Peabody College, Department of Leadership Policy and Organizations, 2023
Course Description
This PhD-level course is structured to provide doctoral students with a foundation in regression analysis that will prepare them for subsequent applied regression and causal inference courses. Topics include the following: hypothesis tests and confidence intervals for population variances; simple linear regression model; testing hypotheses about regression parameters; goodness-of-fit tests; multiple regression model; omitted variable bias; measurement error; and limited dependent variable models (e.g., logit and probit models). An introduction to panel data may also be included, if time permits. The course will make use of Stata, a statistical package, to provide examples of topics learned through lectures.
Learning Objectives
- Formal understanding of econometric theory
- Identify and explain the assumptions underlying regression analysis
- Identify situations when assumptions do not hold and explain how to best address these situations
- Ability to apply regression techniques with a competency sufficient for academic level, empirical research
Practical Skills and Knowledge
- Read and interpret regression output
- Understand how functional form decisions affect the interpetation and magnitude of regression statistics and parameter estimates
- Distinguish between best linear predictor (BLP) and conditional expectation function (CEF) models
- Understand which variables should (and should not) be included when specifying regression equations
- Write programs to implement regression analysis
- Conduct simulations to test regression assumptions