What is multicollinearity and what are its effects?

Question

Accepted Answer

Multicollinearity is a phenomenon where two or more independent variables in a regression model are highly correlated.

It can lead to unstable and unreliable estimates of regression coefficients.
It can make it difficult to determine the individual effect of each independent variable on the dependent variable.
It can also result in inflated standard errors and p-values, making it difficult to identify statistically significant variables.
It can be detected using methods such as correlation matrix, variance inflation factor (VIF), and eigenvalues.
To address multicollinearity, one can remove one of the highly correlated variables, combine them into a single variable, or use regularization techniques such as ridge regression or lasso regression.

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