Several methods can be used to detect multicollinearity:
Variance Inflation Factor (VIF): Measures how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF value greater than 10 is often considered indicative of high multicollinearity. Tolerance: The reciprocal of VIF. A tolerance value less than 0.1 is indicative of high multicollinearity. Correlation Matrix: A simple correlation matrix can reveal high correlations between predictor variables.
