What is multicollinearity? How to tackle this condition?

Question

Accepted Answer

Multicollinearity is a phenomenon in which two or more predictor variables in a regression model are highly correlated.

Multicollinearity can lead to unstable estimates of the coefficients and make it difficult to determine the effect of each predictor variable on the outcome.
One way to tackle multicollinearity is to identify the highly correlated variables and consider removing one of them from the model.
Another approach is to use techniques like principal component analysis or ridge regression to deal with multicollinearity.
Regularization techniques like Lasso regression can also help in reducing the impact of multicollinearity.
Checking the variance inflation factor (VIF) for each predictor variable can also help in identifying multicollinearity.

Top Ipsos Analyst interview questions & answers