Which statement regarding multicollinearity is true?

Multicollinearity refers to a phenomenon in which two or more independent variables in a regression model are highly correlated. This high correlation can make it difficult to ascertain the individual contribution of each predictor variable to explain the variance in the dependent variable.

Identifying multicollinearity is crucial for accurate model interpretation and prediction. Variance Inflation Factors (VIF) are a key tool used in this identification process. Specifically, VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. A high VIF value indicates a high degree of multicollinearity among the predictors, which can lead to unstable coefficient estimates and less reliable statistical tests. Therefore, the use of VIF is a standard and effective method for diagnosing multicollinearity within a dataset, confirming that the statement is indeed true.

In contrast, the incorrect options highlight common misconceptions. While outliers can impact model stability and can exacerbate issues related to multicollinearity, they do not inherently cause it. Multicollinearity does not contribute to improving model accuracy; rather, it can obscure the effects of the independent variables, leading to potential misinterpretation of results. Lastly, multicollinearity is closely related to standard errors, as it