Linear Statistical Models
Regression Lecture Notes


  1. Statistical Tables
  2. Raw Scores, Deviation Scores & Standard Scores
  3. About Summation Signs
  4. Variance, Covariance & Correlation
  5. Linear Regression: An Intuitive Explanation
  6. Simple Linear Regression
  7. Regression Without Predictors
  8. Regression Assumptions
  9. Predicted Scores, Residuals and Other Goodies
  10. Simple Linear Regression Session
  11. A Derivation
  12. Prediction Practice: Part 1
  13. Multiple Linear Regression
  14. "Four Regressions" in Concert
  15. Prediction Practice: Part 2
  16. Data Transformation
  17. Regression Diagnostics
  18. Index Plots
  19. Dichotomous Predictors
  20. Product Variables and Interactions
  21. Multiple Regression Session
  22. Collinearity
  23. Selection and Prediction
  24. Problems with Stepwise Regression
  25. Cross Validation
  26. Polynomial Regression
  27. Partial and Semipartial Correlation
  28. Comparing Regression Models
  29. b vs β
  30. Categorical Predictors
  31. Prediction Practice: Part 3
  32. Ordinal Predictor Variables
  33. Interactions with Categorical Predictors
  34. Centering
  35. Some Scaling Issues
  36. Analysis of Covariance
  37. Aptitude Treatment Interaction
  38. Logistic Regression
  39. Robust Regression
  40. Path Analysis
  41. Linear Structural Models
  42. Specification Error
  43. Regression with Clustered Data
  44. Multilevel Data Issues
  45. Regression with Measurement Error
  46. Nonlinear Regression
  47. Weighted Least Squares
  48. Regression Analysis in Publications
  49. The Regression Zoo
  50. The Matrix
  51. Generalized Linear Models


Linear Statistical Models Course

Phil Ender