What are the effects of multicollinearity in regression analysis?
Multicollinearity occurs when independent variables in a regression model are highly correlated. This can lead to unreliable coefficient estimates.
How does it affect the interpretation of coefficients?
It makes it difficult to determine the individual effect of each predictor on the dependent variable, as their effects are intertwined.
Can it be detected easily?
Yes, it can be detected using Variance Inflation Factor (VIF) or correlation matrices to assess the relationships between predictors.
What are some ways to address multicollinearity?
You can address it by removing highly correlated predictors, combining them, or using techniques like ridge regression.
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