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1) What question does your model address? Is the question a descriptive question or a causal question? Explain why you selected the variables in your model. Is it possible you’ve left out any important variables? Which variables in your model are you interested in and which ones are only there as controls?
Outliers, leverage, and influence:
2) Use the methods outlined in Chapter 11 to examine your data and model for outliers, leverage points, and influence points. • For each of these, consider the impact of any unusual data that you discover on
the results of the analysis and – within your knowledge of the dataset – suggest how the unusual data should be treated.
Non-normality and Non-constant variance
3) Use methods of outlined in Chapter 12 to check for non-normality and non-constant error variance. In each case, attempt to correct any problems that are detected.
• Use relatively simple diagnostics to check for problems and more sophisticated
methods to follow up.
• To check for non-normality, construct a PP-plot and a histogram of the studentized residuals.
• To check for non-constant variance, plot studentized residuals against fitted
Collinearity
4) For each coefficient in your model, find the variance inflation factors (VIF’s). Does collinearity appear to be a serious problem in the model? If so, is there anything reasonable that can be done about it?
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