Linear Regression – Assumption – 2 (Inflection Factor.)

Variance Inflection Factor

The Variance Inflation Factor (VIF) is a statistical measure used to detect multicollinearity in a regression model.
It quantifies how much the variance of a regression coefficient is inflated due to multicollinearity among the independent variables.
In simpler terms, VIF helps identify whether a predictor is strongly correlated with other predictors in the model.

If an independent variable is highly correlated with other variables, the VIF for that variable will be high.
A higher VIF means that the variable is contributing to multicollinearity, making the regression coefficients unreliable.

Detecting Multicollinearity: High VIF values indicate strong multicollinearity, which can lead to unstable and unreliable regression coefficients.
Guiding Variable Selection: By identifying variables with high VIFs, you can decide whether to remove or combine them to reduce multicollinearity.

Drop Highly Correlated Variables: Remove one of the variables with high VIF, especially if it’s redundant or less important.
Combine Variables: Combine correlated variables into a single variable (e.g., their average or principal components).
Regularization: Use techniques like Ridge Regression or Lasso, which are less sensitive to multicollinearity.
Centering: Transform variables by subtracting their mean (mean-centering) to reduce correlation.

The Variance Inflation Factor (VIF) is a valuable diagnostic tool to identify and mitigate multicollinearity in regression models, ensuring that the estimated coefficients remain stable and interpretable.