General Linear Model

1. What does it mean to pick parameter estimates by least squares?
2. Why are least squares estimates desirable, that is, in what sense are they good from an estimation or decision standpoint?
3. What is a treatment effect in ANOVA? (write the equation and explain the terms)
4. What is a fixed-effects model?
5. What is error in a fixed-effects ANOVA model?
6. Why is the sum of squares for error important in ANOVA significance tests?
7. Describe in words the partitioning of variance in a one-way ANOVA.
8. ANOVA makes assumptions about error for significance tests. What are the assumptions?
9. What might happen (why would it be a problem) if the assumption of {normality, equality of error, independence of error} turned out to be false?
10. What is an expected mean square? Why is it important?
11. Why do we use the F test to decide whether means are equal in ANOVA?
12. Correctly interpret ANOVA summary tables.
13. Find correct values of critical F from tabled values for a given test.
14. Suppose someone has worked out that a one-way ANOVA with 6 levels has a power of .80 for the overall F test. What does this mean?
15. Describe (make up) a concrete example of a one-way ANOVA where it makes sense to use an overall F test. Explain why ANOVA (not t, chi-square or something else) is the best method for the analysis.

Testing Means

16. What is the main difference between planned comparisons and post hoc tests?
17. Generate numbers (like 0 1, -1 or 1 –1/2, -1/2) to create a contrast appropriate for a given problem.
18. How many independent comparisons can be made in a given design?
19. What is the difference between a per comparison and a familywise error rate?
20. How does Bonferroni deal with familywise error rate problems?
21. What is the studentized range statistic? How is it used?
22. What is the difference between the Tukey HSD and the Newman-Keuls?
23. What are the considerations when choosing a post hoc test (what do you need to trade-off)?
24. Describe (make up) a concrete example where you would use planned comparisons instead of an overall F test. Explain why the planned comparison is the proper analysis.
25. Describe (make up) a concrete example where you would use a post hoc test. Explain why the post hoc test is needed (not the specific choice of post hoc test, but rather why post hoc test at all).

Factorial ANOVA

26. What are main effects in ANOVA?
27. What are interactions in ANOVA? How do you know you have an interaction?
28. What does it mean for a design to be completely crossed? Balanced? Orthogonal?

29. Describe each term in a linear model like this one:

30. 
    Correctly interpret ANOVA summary tables. Identify mistakes in such tables. What’s the matter with this one?

Source	SS	df	MS	F
A	512	2 (J-1)	128	128
B	108	1(K-1)	108	54
AxB	96	2 (J-1)(K-1)	48	24
Error	12	5 (N-JK)	2

31. Find correct critical values of F from a table for a given design.
32. How does post hoc testing for factorial ANOVA differ from post hoc testing in one-way ANOVA?
33. Describe a concrete example of a two-factor experiment. Why is it interesting and/or important to consider both factors in one experiment?

Random Effects & Repeated Measures

34. What is the difference between fixed- and random-effects in terms of treatments?
35. How are F tests with random effects different than with fixed effects?
36. Describe a concrete example of a randomized block design. You should have 1 factor as the blocking factor and one other factor as the factor of main interest.
37. How is a repeated measures design different from a totally between subjects design in the collection of the data?
38. How does the significance testing change from the totally between to a design to one in which one or more factors are repeated measures (just the general idea, you don’t need to show actual F ratios or computations)?
39. Describe one argument for using repeated measures designs and one argument against using such designs (or describe when you would and would not want to use repeated measures).

Correlation

40. Why does the maximum value of r equal 1.0?

41. What does it mean when a correlation is positive? Negative?

42. What is the purpose of the Fisher r to z transformation?

43. What is range restriction? Range enhancement? What do they do to r?
44. Give an example in which data properly analyzed by ANOVA cannot be used to infer causality.
45. Why do we care about the sampling distribution of the correlation coefficient?

46. What is the effect of reliability on r?

Regression Basics

47. What are predictors and criteria?

48. Write an equation for the linear regression. Describe each term.
49. How do changes in the slope and intercept affect (move) the regression line?
50. What does it mean to test the significance of the regression sum of squares? R-square?
51. What is R-square?
52. What does it mean to choose a regression line to satisfy the loss function of least squares?
53. How do we find the slope and intercept for the regression line with a single independent variable? (Either formula for the slope is acceptable.)
54. Why does testing for the regression sum of squares turn out to have the same result as testing for R-square?