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

Linear Statistical Models Course