A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (Age), the living area of the house in square feet (Living Area) and the number of bedrooms (Bedrooms). The following Excel output shows the partially completed regression output from a random sample of homes that have recently sold. SUMMARY OUTPUT Regression Stotistics Multiple R R Square Adjusted R Square Standard Error Observations 0.8486 36,009.01 ANOVA sS MS F Signi/ficance F 0.0022 Regression Residual Total 36,709,265,905.70 StatP. value p-value Coefficients Standard ErrortStat P. value Lower 95% Upper 95% Intercept Age Living Area Bedrooms 108,597.3721 580.6870 86.8282 31,261.9127 101,922.3333 2,092.4981 27.6994 11,006.8696 0.3095 0.7865 0.0095 0.0161 1) Every additional year in the age of the house will . A) increase the average selling price by $2,092
B) decrease the average selling
C) increase the average selling price by $102
D) decrease the average selling price by $109 price by $581