BIOS 330 Concepts to Master

1. Assumptions of linear additive models
2. Methods for checking these assumptions.
3. Global vs. partial tests of association
4. Multiple ways of computing test statistics in multiple regression models that were fitted using ordinary least squares
5. Dummy variables and how their corresponding regression coefficients are interpreted
6. Interpretation of interaction effects
7. Assumptions of interaction tests
8. Writing null hypotheses precisely in terms of parameters being tested
9. Understanding tests for the overall association of a predictor with the response, and how to test sub-hypotheses such as linearity
10. Combined partial tests for multiple predictors
11. Combined tests for overall effects of a predictor when it interacts with other predictors
12. Regression splines (linear, cubic, and restricted cubic) and knots
13. How knots are chosen
14. How the number of knots relates to the flexibility allowed for the fit
15. Which if any terms of a predictor that is expanded into multiple constructed variables can be tested singly
16. What tests of effects, interactions, and nonlinearity are powered to detect
17. Nonparametric smoothers
18. Problems with naive approaches of handing missing data
19. The effect of changing how models are fitted based on looking at the data
20. Deciding on the number of degrees of freedom to "spend" in a model, and where to spend them
21. Understand regression to the mean
22. Have an initial understanding of data reduction
23. Elements of bootstrapping
24. Model validation approaches and which methods of validation are most stringent.
25. How to display a complex regression model to a non-statistician.
26. How to make a complex nonlinear relationship a non-issue to the reader.
27. A principle for estimating unknown parameters when least squares is not appropriate.
28. What is a Wald statistic and a likelihood ratio statistic in general terms, and which one works better.
29. When chi-square statistics are used instead of t or F statistics, and how to approximately relate a chi-square statistic to an F statistic.
30. Exact interpretation of logistic model coefficients in the linear regression case.
31. Assumptions of binary logistic regression.
32. The value and use of a nonparametric smoother in examining logistic model assumptions or in determining shapes of relationships when Y is binary.
33. How to convert between probabilities, odds, and log odds.
34. Measures of predictive accuracy and predictive ability for binary logistic models.
35. What is meant by an ordinal response variable and what is assumed about the data when you use a model or a rank test on an ordinal response.
36. How to interpret coefficients in proportional odds models.
37. What about odds ratios is assumed by the proportional odds model.
38. How are ordinary nonparametric rank tests relate to the proportional odds model.
39. What is the value of only using the ordering of Y.