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 Statistics Multiple R 0.8486 R Square Adjusted R Square Standard Error 36,009.01 Observations ANOVA df MS F SS 36,709,265,905.70 Significance F 0.0022 Regression Residual Total 14 50,972,400,000.00 t Stat Lower 95% Upper 95% Intercept Age Living Area Bedrooms Coefficients 108,597.3721 -580.6870 86.8282 31,261.9127 Standard Error 101,922.3333 2,092.4981 27.6994 11,006.8696 P-value 0.3095 0.7865 0.0095 0.0161 Based on the outputs, we can state that every additional year in the age of the house would A increase the average selling price by $2,092, after keeping other independent variables constant B increase the selling price by $102 on average, after keeping other independent variables constant decrease the average selling price by $109, after keeping other independent variables constant decrease the selling price by expected value of $581, after keeping other independent variables constant