What methods can be used to detect multicollinearity?

The identification of multicollinearity in regression analysis involves scrutinizing the relationships between independent variables to determine whether they are highly correlated with one another, which can distort the statistical significance of predictors.

Using the correlation matrix allows one to visually assess the pairwise relationships among variables. High correlation coefficients indicate a potential multicollinearity issue. In addition to this, calculating variance inflation factors (VIF) provides a quantitative measure of how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF value greater than 10 is typically considered indicative of significant multicollinearity.

By combining these two methods—analyzing the correlation matrix and computing VIFs—you obtain a robust approach to detecting multicollinearity issues, making this method comprehensive. The other options either rely solely on one method or suggest incomplete strategies that do not encompass the full range of techniques available for detecting multicollinearity.