How to address multicollinearity

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. This can lead to skewed results; for example, the regression coefficients may not be reliable and the standard errors may be too high. To address multicollinearity, you can use a variety of techniques. The most popular techniques are: - Removing one of the problematic variables from the regression model. - Adding polynomial terms to the model to capture non-linear relationships between the variables. - Performing a principal component analysis (PCA) to reduce the number of variables. - Applying a regularization technique such as ridge or lasso regression. - Transforming the data, such as through normalizing or standardizing the variables. No matter which technique you use, you must always assess the model to verify that multicollinearity is resolved.