The Variance Inflation Factor (VIF) is a statistical measure used to assess how much the variance of a regression coefficient is inflated due to multicollinearity among the independent variables. A high VIF indicates that a variable is highly correlated with one or more other variables, which can distort the results of a regression analysis. Typically, a VIF value above 10 suggests significant multicollinearity, prompting analysts to consider removing or combining variables. By addressing multicollinearity, researchers can improve the reliability of their regression models, leading to more accurate predictions and insights about the relationships between the variables involved.