An analyst wants to build a regression model to predict spending from the following four predictor variables: Past Spending, Income, Net Worth, and Age. The analyst, worried about collinearity, regresses Age against Past Spending, Income, and Net Worth. The output is displayed in the accompanying table. What is the VIF for Age? E Click the icon to view the regression output. VIF = :| (Round to two decimal places as needed.) Regression model output Response Variable: Age R2 = 99.50% Adjusted R2 = 99.50% s = 2.189 with 908 - 4 = 904 degrees of freedom Variable Coeff SE(Coeff) t-ratio (Intercept) 2.017e + 01 1.433e - 01 133.941 Past Spending 3.322e 04 1.869e 04 1.674 Income 3.881 e - 04 7.635e - 06 50.941 Networth 2.446e 05 1.486e - 06 16.905