Multicollinearity Detection and its Interpretation in R
Multicollinearity is the existence of strong correlations between two or more predictor variables in a multiple regression model. When a researcher or analyst tries to figure out how each independent variable might be utilized to predict or comprehend the dependent variable in a statistical model, multicollinearity can result in skewed or misleading results.
Different methods of detecting multicollinearity
1. High R^2 but few significant t ratios
2. High pairwise correlations among the predictors
3. Determination of partial correlations
4. Auxiliary regressions
5. Eigen value and condition index
6. Tolerance and Variance Inflation Factor (VIF)