Interpreting (Even Tricky) Regression Coefficients – A Quiz

by Karen Grace-Martin

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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Interpreting Linear Regression Coefficients: A Walk Through Output
Learn the approach for understanding coefficients in that regression as we walk through output of a model that includes numerical and categorical predictors and an interaction.

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