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Multicollinearity occurs when two or more independent variables in a regression model are highly correlated. This can lead to issues such as inflated standard errors, unreliable statistical tests, and difficulty in determining the effect of each predictor. Here are various methods to evaluate multicollinearity:
Evaluating multicollinearity is crucial for ensuring the reliability of regression models. By using a combination of these methods, data scientists can identify and address multicollinearity, leading to more robust and interpretable models.