Wednesday, Jan 14. Introduction.
Friday, Jan 16. Linear models.
Monday, Jan 19. Holiday.
Wednesday, Jan 21. Introduction to
R and the lm function for specifying linear models.
Friday, Jan 23. Linear functions of the parameters of a linear model.
Monday, Jan 26. Using the
contrast function for inferences concerning linear
functions of model parameters, plotting estimated expected responses
with ggplot.
Wednesday, Jan 28. Inference for linear models.
Friday, Jan 30. Inference for linear models — continued.
Monday, Feb 2. In-class demonstration of R workflow with specification and inference for linear models.
Wednesday, Feb 4. Inferences w/visualization for expected responses and predictions.
Friday, Feb 6. Marginal means.
Monday, Feb 9. Modeling nonlinearity (with linear models).
Wednesday, Feb 11. Introduction to nonlinear regression.
Friday, Feb 13. Nonlinear regression — continued.
Monday, Feb 16. Holiday.
Wednesday, Feb 18. Assumptions.
Friday, Feb 20. Assumptions — continued.
Monday, Feb 23. Solutions for heteroscedasticity: variance-stabilizing transformations, weighted least squares.
Wednesday, Feb 25. Iteratively weighted least squares, parametric models for heteroscedasticity, robust standard errors.
Friday, Feb 27. In-class demonstration of nonlinear regression and dealing with heteroscedasticity.
Monday, Mar 2. Introduction to Poisson regression and generalized linear models.
Wednesday, Mar 4. Poisson regression — continued, rate ratios.
Friday, Mar 6. Modeling rates with Poisson regression.
Monday, Mar 9. Introduction to logistic regression.
Wednesday, Mar 11. Logistic regression — continued.
Friday, Mar 13. Using the emmeans package, the relationship between logistic and Poisson regression for rare events, and separation.
Monday, Mar 16. Spring recess.
Wednesday, Mar 18. Spring recess.
Friday, Mar 20. Spring recess.
Monday, Mar 23. In-class demonstration of Poisson and logistic regression.
Wednesday, Mar 25. Maximum likelihood, likelihood ratios, profile likelihood, Wald tests and confidence intervals, assumptions.
Friday, Mar 27. Over-dispersion.
Monday, Mar 30. Negative binomial models, robust standard errors for GLMs, gamma regression.
Wednesday, Apr 1. Alternative link functions.
Friday, Apr 3. Marginal effects.
Monday, Apr 6. The delta method.
Wednesday, Apr 8. Introduction to survival analysis, accelerated failure time models.
Friday, Apr 10. Censoring, survival functions.
Monday, Apr 13. Hazard functions, proportional hazards models.
Wednesday, Apr 15. Proportional hazards models — continued, discrete survival time models.
Friday, Apr 17. Discrete survival models — continued, introduction to categorical response variable regression models.
Monday, Apr 20. Sequential categorical regression models, proportional odds models.
Wednesday, Apr 22.
Friday, Apr 24.
Monday, Apr 27.
Wednesday, Apr 29.
Friday, May 1.
Monday, May 4.
Wednesday, May 6.
Friday, May 8.