Decision Analysis, Modeling, and Quantitative Methods

4 credits · Graduate

This course covers intermediate quantitative methods for public policy analysis. The primary learning goals for the course are that students understand how to 1) implement and interpret linear regression; 2) distinguish correlation from causation; 3) code descriptive statistics, regressions, and data visualizations; and 4) engage in self-learning of additional methods depending on the student’s research interests. We will also learn, in a mostly conceptual way, about more advanced linear models and ‘quasi-experimental’ research design such that students leave the course as effective consumers of these methods.