The VIF's for all three variables are all incredibly small, indicating that there is little to no multicollinearity in Model 2. Example 3 with HOUSEDATA: You try! What happens when we create a multiple linear regression with the HOUSEDATA data set where the Y variable is PRICE and the X-variables are SIZE, AGE, SPEC.FEATS, and TRAIN.DIST? Module 4 Question 15 Homework * Unanswered = Due Today, 5.50 PM For the linear regression model Predicted price (thousands) =b0+b1(S12E)+b2(AGE)+b3( SPEC.FEATS )+b4( TRAIN.DIST) What is the VIF associated with the variable SIZE? Round to at fast two decimal places. Console Terminal x Jobs +1 > HOUSEMOdel_HW Tibrary (car) > vif(HOUSEModel HW) SIZE AGE SPEC. FEATS TRAIN. DIST 1.3604551.0053531.3679961.006926 The VIF's for all three variables are all incredibly small, indicating that there is little to no multicollinearity in Model 2. Example 3 with HOUSEDATA: You try! What happens when we create a multiple linear regression with the HOUSEDATA data set where the Y variable is PRICE and the X-variables are SIZE, AGE, SPEC.FEATS, and TRAIN.DIST? Module 4 Question 15 Homework * Unanswered = Due Today, 5.50 PM For the linear regression model Predicted price (thousands) =b0+b1(S12E)+b2(AGE)+b3( SPEC.FEATS )+b4( TRAIN.DIST) What is the VIF associated with the variable SIZE? Round to at fast two decimal places. Console Terminal x Jobs +1 > HOUSEMOdel_HW Tibrary (car) > vif(HOUSEModel HW) SIZE AGE SPEC. FEATS TRAIN. DIST 1.3604551.0053531.3679961.006926