• Numerical Variables
• Categorical variables
• Variance Inflation Factor of Numerical Variables

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In order to detect multicollinearity, we introduced The Variance Inflation Factor, which is a measure of collinearity among predictor variables within a multiple regression. It is calculated by taking the the ratio of the variance of all a given model's betas divide by the variance of a single beta if it were fit alone. As a rule of thumb, VIF values in excess of 5 or 10 are often considered an indication that multicollinearity may by a cause of problem.