A home appraisal company would like to develop a regression model that would predict the selling price of a house


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.
Regression Statistics
Multiple R 0.8486
R Square
Adjusted R Square
Standard Error 36,009.01
Observations
ANOVA
df SS MS F Significance F
Regression 36,709,265,905.70 0.0022
Residual
Total 14 50,972,400,000,00
Coefficents Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 108,597.3721 101,922.3333 0.3095
Age -580.6870 2,092.4981 0.7865
Living Area 86.8282 27.6994 0.0095
Bedrooms 31,261.9127 11,006.8696 0.0161
The F-test statistic for the overall regression model is:


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