How can centering variables help in interpreting interaction effects in Multiple Linear Regression?
- By increasing model accuracy
- By increasing prediction speed
- By reducing multicollinearity between main effects and interaction terms
- By simplifying the model
Centering variables (subtracting the mean) can reduce multicollinearity between main effects and interaction terms, making it easier to interpret the individual and combined effects of the variables.
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