STAT 510 Lecture Notes

Spring 2019

  1. Preliminaries
  2. Some Key Linear Models Results     Projection Example
  3. The F Test for Comparing Reduced vs. Full Models     Annotations
  4. Analysis of Two-Factor Experiments Based on Cell Means Models
  5. Analysis of Two-Factor Experiments Based on Additive Models
  6. Analysis of Variance (ANOVA)
  7. Analysis of Variance for Balanced Two-Factor Experiments
  8. Analysis of Variance for Unbalanced Two-Factor Experiments
  9. Orthogonal Linear Combinations, Contrasts, and Additional Partitioning of ANOVA Sums of Squares
  10. The Aitken Model
  11. Linear Mixed-Effects Models
  12. The ANOVA Approach to the Analysis of Linear Mixed-Effects Models
  13. The Cochran-Satterthwaite Approximation for Linear Combinations of Mean Squares
  14. Linear Mixed-Effects Models for Data from Split-Plot Experiments
  15. ANOVA for Balanced Split-Plot Experiments
  16. SAS Analysis of Split-Plot Experiments     Annotated Version
  17. R Analysis of Split-Plot Experiments
  18. More Example Split-Plot Experiments
  19. Maximum Likelihood Estimation for the General Linear Model
  20. REML Estimation of Variance Components
  21. Best Linear Unbiased Prediction (BLUP) of Random Effects in the Normal Linear Mixed Effects Model
  22. Additional Topics Related to Likelihood
  23. Repeated Measures
  24. R Code for Repeated Measures
  25. SAS Code for Repeated Measures
  26. A Generalized Linear Model for Bernoulli Response Data
  27. A Generalized Linear Model for Binomial Response Data
  28. A Generalized Linear Model for Poisson Response Data
  29. Generalized Linear Mixed-Effects Models