One use of VIF in multiple regression is deciding which variable to include in a model.

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VIF (Variance Inflation Factor) is a measure of how much the variance of the estimated regression coefficient is increased due to collinearity among the independent variables. A high VIF value indicates strong correlation between two or more independent variables, and it suggests that these variables might be redundant in the model. Therefore, VIF can help in deciding which variable to include in a model by identifying highly correlated variables that can be removed to improve the model's performance.

One Use of VIF in Multiple Regression Is Deciding Which