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Can Variance Inflation factors be used to detect Heteroscedasticity .pdf

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Can Variance Inflation factors be used to detect Heteroscedasticity problem in the regression analysis Solution Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. Use to describe how much multicollinearity (correlation between predictors) exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret. Use the following guidelines to interpret the VIF:VIFStatus of predictorsVIF = 1Not correlated1 < VIF < 5Moderately correlatedVIF > 5 to 10Highly correlated.

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Can Variance Inflation factors be used to detect Heteroscedasticity problem in the regression analysis Solution Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. Use to describe how much multicollinearity (correlation between predictors) exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret. Use the following guidelines to interpret the VIF:VIFStatus of predictorsVIF = 1Not correlated1 < VIF < 5Moderately correlatedVIF > 5 to 10Highly correlated

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  • 1. Can Variance Inflation factors be used to detect Heteroscedasticity problem in the regression analysis Solution Variance inflation factors (VIF) measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. Use to describe how much multicollinearity (correlation between predictors) exists in a regression analysis. Multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret. Use the following guidelines to interpret the VIF:VIFStatus of predictorsVIF = 1Not correlated1 < VIF < 5Moderately correlatedVIF > 5 to 10Highly correlated