Description: Variance inflation refers to the increase in the variance of an estimator that occurs as a result of multicollinearity among independent variables in a regression model. This phenomenon arises when two or more predictor variables are highly correlated, making it difficult to accurately estimate the coefficients of the model. Consequently, the variance of the estimators increases, leading to wider confidence intervals and a reduced ability to detect significant effects. Variance inflation is commonly measured using the Variance Inflation Factor (VIF), which quantifies how much the variance of a coefficient is increased compared to a model without multicollinearity. A VIF greater than 10 is generally considered indicative of serious multicollinearity issues. This concept is crucial in regression analysis across various fields, as high variance inflation can compromise the validity of inferences drawn from the model, affecting the interpretation of results and decision-making based on them.
