Which method is typically used to identify multicollinearity issues in regression?

The Variance Inflation Factor (VIF) is a key tool used to detect multicollinearity in regression analysis, making it the correct choice. Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, which can lead to unreliable and unstable estimates of regression coefficients.

The VIF quantifies how much the variance of an estimated regression coefficient increases due to multicollinearity. Specifically, it assesses how much of the variance of a specific predictor is inflated due to correlations with other predictors. A VIF value greater than 10 (although some use a threshold of 5) typically indicates a problematic amount of multicollinearity that may need to be addressed.

When considering other methods mentioned in the options, adjusted R^2 analysis evaluates the explanatory power of the model and adjusts for the number of predictors but does not give direct insights into multicollinearity. The Durbin-Watson statistic helps detect the presence of autocorrelation in the residuals of the regression but is not related to multicollinearity. Residual plots visualize the relationship between observed and predicted values, which can indicate potential issues in model fit but do not specifically assess multicollinearity. Therefore, the VIF stands out as