Average selling price A home appraisal company would like to develop a regressio

Question

Average selling price 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. Every additional year in the age of the house will increase the average selling price by $2,092 decrease the average selling price by $581 increase the average selling price by $102 decrease the average selling price by $109

Explanation / Answer

Sol)

Every addinional year in the age of the house will be decrease the average selling price by 581

SUMMARY OUTPUT Regression Statistics Multiple R 0.8486 R Square 0.720122 Adjusted R Square 0.6081 Standard Error 36,009.01 Observations 15 ANOVA df SS MS F Significance F Regression 3 36709265906 12236421969 9.436961103 0.0022 Residual 11 14263134094 1296648554 Total 14 50972400000 Coefficients Standard Error t Stat P-value Intercept 108597.4 101922.333 1.065491428 0.3095 Age -580.687 2092.4981 -0.27750897 0.7865 Living Area 86.8282 27.6994 3.134659957 0.0095 Bedrooms 31261.91 11006.8696 2.840218321 0.0161

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