Here’s a little quiz:
True or False?
1. When you add an interaction to a regression model, you can still evaluate the main effects of the terms that make up the interaction, just like in ANOVA.
2. The intercept is usually meaningless in a regression model.
3. In Analysis of Covariance, the covariate is a nuisance variable, and the real point of the analysis is to evaluate the means after controlling for the covariate.
4. Standardized regression coefficients are meaningful for dummy-coded predictors.
5. The only way to evaluate an interaction between two independent variables is to categorize one or both of them.
They’re all false.
(I’ll post the reasons tomorrow).
These are some of the biggest misconceptions among researchers using Regression and Analysis of Covariance I’ve come across over the years.
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