How can I reduce multicollinearity in my regression model?

How can I reduce multicollinearity in my regression model?

You can reduce multicollinearity by removing highly correlated predictors, using regularization techniques, or combining variables.

What are some regularization techniques I can use?

Common regularization techniques include Ridge regression and Lasso regression, which help to penalize large coefficients.

How do I identify multicollinearity in my data?

You can identify multicollinearity using Variance Inflation Factor (VIF) or by examining the correlation matrix of your predictors.

Is it okay to drop variables to reduce multicollinearity?

Yes, dropping variables can be effective, but ensure that the removed variables are not crucial for your model's predictive power.