Statistics 511 Lecture Notes

Spring 2012

Statisitics 511 Home Page
Dan Nettleton's Home Page

Lecture Notes

  1. Introduction to the Gauss-Markov Linear Model
  2. Estimation of the Response Mean
  3. Estimating Estimable Functions of Beta
  4. Proof of the Gauss-Markov Theorem
  5. Example Estimable Functions
  6. R Code and Output for Estimating Estimable Functions
  7. Alternative Parameterizations
  8. The Distribution of the Ordinary Least Squares Estimator
  9. Estimation of Error Variance under the Gauss-Markov Model
  10. Inference for Estimable Functions of Beta under the Normal Theory Gauss-Markov Model
  11. Unbalanced Two-Factor Experiment Example R Code
  12. The Power of the F-Test
  13. The Reduced vs. Full Model F-Test
  14. Reduced vs. Full Model F Test in R
  15. Analysis of Variance Tables
  16. Analysis of Variance for Orthogonal Polynomial Contrasts
  17. ANOVA for the Unbalanced Two Factor Experiment
  18. The Aitken Model
  19. An Example Analysis Based on the Aitken Model
  20. Introduction to Linear Mixed Effects Models
  21. Correspondence between Experimental Design and Mixed-Effects Models
  22. The ANOVA Approach to the Analysis of Linear Mixed Effects Models
  23. The Cochran-Satterthwaite Approximation for Linear Combinations of Mean Squares
  24. ANOVA Analysis of the Balanced Split Plot Experiment
  25. Maximum Likelihood Estimation in the General Linear Model
  26. REML Estimation of Variance Components
  27. Best Linear Unbiased Prediction (BLUP) of Random Effects in the Normal Linear Mixed Effects Model
  28. Simulation and Analysis of Data from a Classic Split Plot Experimental Design
  29. Miscellaneous Topics Related to Likelihood
  30. Repeated Measures
  31. Repeated Measures in R
  32. Nonlinear Models
  33. Generalized Linear Models
  34. Smoothing Scatterplots Using Penalized Splines
  35. An Introduction to the Bootstrap

Last Modified