Wednesday, Jan 8. Introduction.
Friday, Jan 10. Linear models, indicator variables.
Monday, Jan 13. Introduction to R
and the lm function for specifying linear models.
Wednesday, Jan 15. Linear functions
of model parameters, using the lincon function.
Friday, Jan 17. Using the
contrast function for inferences concerning linear
functions of model parameters, plotting estimated expected responses
with ggplot.
Monday, Jan 20. Holiday.
Wednesday, Jan 22. Inference for linear models.
Friday, Jan 24. Inference for linear models — continued.
Monday, Jan 27. R workflow demonstration for linear models.
Wednesday, Jan 29. Estimated expected response, prediction intervals, and some visualization.
Friday, Jan 31. Marginal means.
Monday, Feb 3. Modeling nonlinearity.
Wednesday, Feb 5. Nonlinear regression.
Friday, Feb 7. Nonlinear regression — continued.
Monday, Feb 10. Assumptions.
Wednesday, Feb 12. Assumptions — continued.
Friday, Feb 14. Heteroscedasticity, weighted least squares, and iteratively weighted least squares.
Monday, Feb 17. Holiday.
Wednesday, Feb 19. Iteratively weighted least squares — continued, parametric models for heteroscedasticity, robust standard errors.
Friday, Feb 21. In-class demonstration of nonlinear regression and dealing with heteroscedasticity.
Monday, Feb 24.
Wednesday, Feb 26.
Friday, Feb 28.
Monday, Mar 3.
Wednesday, Mar 5.
Friday, Mar 7.
Monday, Mar 10. Spring recess.
Wednesday, Mar 12. Spring recess.
Friday, Mar 14. Spring recess.
Monday, Mar 17.
Wednesday, Mar 19.
Friday, Mar 21.
Monday, Mar 24.
Wednesday, Mar 26.
Friday, Mar 28.
Monday, Mar 31.
Wednesday, Apr 2.
Friday, Apr 4.
Monday, Apr 7.
Wednesday, Apr 9.
Friday, Apr 11.
Monday, Apr 14.
Wednesday, Apr 16.
Friday, Apr 18.
Monday, Apr 21.
Wednesday, Apr 23.
Friday, Apr 25.
Monday, Apr 28.
Wednesday, Apr 30.
Friday, May 2.