Understanding Multicollinearity in Regression Analysis

In multiple regression analysis, multicollinearity occurs when two or more independent variables are highly correlated with each other, which can lead to inefficiencies in estimating the coefficients of these variables. One way to detect multicollinearity is through the Variance Inflation Factor (VIF). A VIF value greater than 10 is often considered indicative of problematic multicollinearity.

Given this context, select the correct statement regarding the implications of multicollinearity in a regression model.