What term describes the correlation among independent variables in a multiple regression model?

Multicollinearity refers to a situation in a multiple regression model where two or more independent variables are highly correlated with each other. This correlation can make it difficult to determine the individual effect of each independent variable on the dependent variable. When multicollinearity is present, it can lead to unreliable coefficient estimates, inflated standard errors, and affect hypothesis testing, making it hard to identify which predictors are significant contributors to the model. A common consequence of multicollinearity is that the model may perform well in predicting the dependent variable but provide misleading interpretations of the relationships between the variables involved.

Understanding multicollinearity is crucial in regression analysis because it directly impacts the quality of the model and the validity of conclusions drawn from it. Effective strategies to address multicollinearity include removing one of the highly correlated variables, combining them into a single variable, or using dimension reduction techniques like principal component analysis. This highlights why identifying and managing multicollinearity is key in ensuring robust regression analysis results.