Teaching

Graduate methods course readers for the University of Arizona, written to accompany the texts each course is built around.

POL 682 · Linear Regression Analysis

Graduate methods · Fall · University of Arizona

First semester. The linear model from the ground up: least squares, the Gauss–Markov assumptions, inference, and what to do when the assumptions fail. Worked in R throughout, with a reproducible workflow.

Open the POL 682 reader

What the reader covers

  • Foundations: simple regression, BLUE, estimation, and the matrix form of the model.
  • Inference: variable selection, interactions, and interpretation.
  • When assumptions fail: heteroskedasticity, multicollinearity, autocorrelation, measurement error, and endogeneity.
  • Applications: panel data, plus a documented data appendix.

Companion texts — read alongside the reader

  • Fox, John (2016). Applied Regression Analysis and Generalized Linear Models (3rd ed.). Sage.
  • Fox, John, and Sanford Weisberg (2019). An R Companion to Applied Regression (3rd ed.). Sage.
  • Gelman, Andrew, and Jennifer Hill (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

POL 683 · Advanced Regression & Causal Inference

Graduate methods · Spring · University of Arizona

Second semester. What to do when the outcome is not continuous and the design is not experimental: limited dependent variables, simulation as a tool for understanding models, and the causal inference toolkit.

Open the POL 683 reader

What the reader covers

  • Inference and matrix algebra, revisited as the working language of the sequence.
  • Limited dependent variables: binary, ordinal, multicategory, and count outcomes.
  • Simulation for interpretation, prediction, and uncertainty.
  • Causal inference and model fit.

Companion texts — read alongside the reader

  • Long, J. Scott (1997). Regression Models for Categorical and Limited Dependent Variables. Sage.
  • McElreath, Richard (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd ed.). CRC Press.
  • Gelman, Andrew, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin (2014). Bayesian Data Analysis (3rd ed.). CRC Press.