What is multicollinearity in the context of regression models?

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. This high correlation can create problems in estimating the coefficients of the regression model effectively. When multicollinearity is present, it becomes challenging to determine the individual effect of each independent variable on the dependent variable because the variables do not provide unique information. Instead, they may duplicate information, leading to inflated standard errors and unstable estimates. This can ultimately make the model less interpretable and reduce the reliability of the conclusions drawn from the analysis. Recognizing and addressing multicollinearity is essential for ensuring that regression results are valid and can provide accurate insights into the relationships being examined.