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Average Course Rating

Data-driven decision making in public policy often investigates the causes of problems and the effects of policies. However, causal identification is extremely difficult, and the application of statistical processes can often be misleading. The course builds on the foundation from prerequisite courses and provides a more rigorous and faster-paced survey of statistics, regression modeling, and research design. Specific topics include measures of fit, generalized linear models, interaction terms, model specification, and common econometric tools like instrumental variables or difference in difference designs. The goal of the course is to use quantitative data in an applied manner to address meaningful research questions. Prerequisites: 470.681 Probability and Statistics and 470.768, Programming and Data Management (470.768 may be taken concurrently with 470.709).