This is the first course in a PhD-level quantitative methods sequence and provides an introduction to the tools used in basic quantitative social science research. The first four weeks of the course cover introductory univariate statistics, while the remainder of the course focuses on linear regression models. Furthermore, the principles learned in this course provide a foundation for the future study of more advanced topics in quantitative political methodology.

• Syllabus

Lecture notes (2016):

• Introduction: Slides
• Random Variables and Probability Distributions: Slides Course Notes
• Multiple Random Variables: Slides Course Notes
• Sums, Means, and Limit Theorems: Slides Course Notes
• Estimation and Statistical Inferences: Slides Course Notes
• Hypothesis Tests: Slides Course Notes
• What is Regression: Slides Course Notes
• Simple Linear Regression: Slides Course Notes
• Linear Regression with Two Covariates: Slides Course Notes
• Multiple Regression in Matrix Form: Slides Course Notes
• Interactions, Non-linearity, and the F-test: Slides Course Notes
• Troubleshooting the Linear Model: Slides
• Panel Data and Clustering: Slides Course Notes

Lecture notes (2015):

• Introduction: Slides
• Random Variables and Probability Distributions: Slides Handout
• Multiple Random Variables: Slides Handout
• Univariate Inference I (Point Estimation): Slides Handout
• Univariate Inference II (Intervals and Testing): Slides Handout
• What Is Regression?: Slides Handout
• Simple Linear Regression: Slides Handout
• Linear Regression with Two Covariates: Slides Handout
• Interactions, F-tests, and Non-linearities: Slides Handout
• Linear Regression in Matrix Form: Slides Handout
• Diagnostics I (Nonnormality, Nonlinearity, and Outliers): Slides Handout
• Diagnostics II (Heteroskedasticity): Slides Handout

Data from Lecture Slides:

• Gerber, Green, and Larimer Data
• Fulton County Data
• Weight Data
• Fitbit Data
• Acemoglu, Johnson, and Robinson Data
• Fish Data
• Nunn and Wantchekon Data