How can you test for multicollinearity in Simple Linear Regression, and why is it important?
- By Checking Accuracy, Improves Prediction
- By Checking Residuals, Reduces Overfitting
- By Checking Variance Inflation Factor (VIF), Prevents Unstable Estimates
- By Examining Correlations between Variables, Prevents Confounding Effects
Multicollinearity can be detected by checking the Variance Inflation Factor (VIF). It is important as multicollinearity can lead to unstable estimates and make it difficult to interpret individual coefficients.
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