How does multicollinearity affect the performance of a Multiple Linear Regression model?
- Enhances prediction accuracy
- Increases bias
- Makes coefficients unstable
- Simplifies the model
Multicollinearity can make the coefficient estimates unstable and unreliable, causing difficulty in interpreting the individual effect of each predictor.
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