Wednesday, Jan 10. Introduction.
Tuesday, Jan 23. Linear models.
Monday, Jan 15. Holiday.
Wednesday, Jan 17. Introduction to
R and the lm function for specifying a linear
model.
Tuesday, Jan 30. Specifying linear models — continued, linear combinations of parameters.
Monday, Jan 22. Using the
contrast function, plotting data and models.
Wednesday, Jan 24. Statistical inference for linear models.
Tuesday, Feb 6. Statistical inference for linear models — continued.
Monday, Jan 29. R workflow demonstration for linear models.
Wednesday, Jan 31. Estimated expected response, prediction intervals, and some visualization.
Tuesday, Feb 13. Estimating marginal means.
Monday, Feb 5. Modeling nonlinear relationships with linear models.
Wednesday, Feb 7. Introduction to nonlinear regression.
Tuesday, Feb 20. Nonlinear regression — continued.
Monday, Feb 12. Assumptions, residuals, and heteroscedasticity.
Wednesday, Feb 14. Assumptions — continued.
Tuesday, Feb 27. Solutions for heteroscedasticity — variance stabilizing transformations, weighted least squares.
Monday, Feb 19. Holiday.
Wednesday, Feb 21. Iteratively weighted least squares, parametric models for heteroscedasticity, heteroscedastic consistent standard errors.
Tuesday, Mar 5. R workflow demonstration for nonlinear regression and heteroscedasticity.
Monday, Feb 26. Introduction to Poisson regression and generalized linear models.
Wednesday, Feb 28. Interpretation of GLMs with log link functions, rate ratios.
Tuesday, Mar 12. Using an offset variable with Poisson regression.
Monday, Mar 4. Introduction to logistic regression.
Wednesday, Mar 6. Logistic regression — continued, odds ratios.
Tuesday, Mar 19. Using the emmeans package with Poisson and logistic regression models, the relationship between Poisson and logistic regression, separation.
Monday, Mar 11. Spring recess.
Wednesday, Mar 13. Spring recess.
Tuesday, Mar 26. Spring recess.
Monday, Mar 18. Demonstration of Poisson and logistic regression.
Wednesday, Mar 20. Inference based on maximum likelihood.
Tuesday, Apr 2. Over-dispersion, quasi-likelihood.
Monday, Mar 25. Distributions for over-dispersion, gamma and inverse-gaussian generalized linear models.
Wednesday, Mar 27. Alternative link functions for generalized linear models.
Tuesday, Apr 9. Marginal effects.
Monday, Apr 1. The delta method.
Wednesday, Apr 3. Introduction to survival analysis, accelerated failure time models.
Tuesday, Apr 16. Accelerated failure time models — continued, censoring, survival function.
Monday, Apr 8. Hazard functions, proportional hazards models.
Wednesday, Apr 10. Proportional hazards models, discrete survival models.
Tuesday, Apr 23. Discrete survival models and sequential categorical regression models.
Monday, Apr 15. Sequential regression models — continued, proportional odds models.
Wednesday, Apr 17. Multinomial logit regression models for unordered categorical response variables.
Tuesday, Apr 30. Introduction to the incidental parameter problem, marginal models and GEE.
Monday, Apr 22. The fixed effects approach to the incidental parameter problem.
Wednesday, Apr 24. Introduction to the random effects approach to the incidental parameter problem.
Tuesday, May 7. Random effects — continued, nonlinear regression with random effects, crossed and nested random effects.
Monday, Apr 29. Model selection via prediction error, cross-validation, and AIC.
Wednesday, May 1. Generalized additive models.
Tuesday, May 14. Generalized additive models — continued.