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.
Answers:
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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