What are the consequences of ignoring multicollinearity in a Multiple Linear Regression model?
- Improved efficiency
- Increased accuracy
- Simpler model
- Unstable coefficients, difficulties in interpretation
Ignoring multicollinearity can lead to unstable coefficient estimates and difficulties in interpreting the individual effect of predictors, reducing the model's reliability and interpretability.
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