We will build on the basic knowledge of statistical methods from the previous quarter to further develop fundamentals for the design, implementation and interpretation of policy-relevant research. We will discuss the statistical formulation and practical applications of linear regression analysis, the assumptions of OLS models, and how to check and address violations of these assumptions. We will consider models for dichotomous and categorical dependent variables including logit and problt regression. We will also discuss specialized topics including causal inference strategies (such as fixed effects, instrumental variables, and regression discontinuity), missing data, and data reduction techniques such as factor analysis. Students will conduct their own empirical research using quantitative analysis. Once again, we will make extensive use of Stata software. Core course so enrollment is limited to MIP students
5 units · Letter (ABCD/NP)
We will build on the basic knowledge of statistical methods from the previous quarter to further develop fundamentals for the design, implementation and interpretation of policy-relevant research. We will discuss the statistical formulation and practical applications of linear regression analysis, the assumptions of OLS models, and how to check and address violations of these assumptions. We will consider models for dichotomous and categorical dependent variables including logit and problt regression. We will also discuss specialized topics including causal inference strategies (such as fixed effects, instrumental variables, and regression discontinuity), missing data, and data reduction techniques such as factor analysis. Students will conduct their own empirical research using quantitative analysis. Once again, we will make extensive use of Stata software. Core course so enrollment is limited to MIP students
Offered in Winter 2026 at Stanford University.