What are some of the adverse impacts of Multicollinearity on the coefficients of a linear regression model?
- All of the above.
- It inflates the standard errors of the coefficients.
- It makes the model unstable.
- It weakens the statistical power of the model.
Multicollinearity affects the coefficients of a linear regression model by making them unstable (small changes in the data cause large swings in the coefficients), inflating the standard errors of the coefficients (making them less statistically significant), and weakening the statistical power of the model (decreasing the chances of finding valid effects).
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