Use the following to answer questions 24 - 26. First City Real Estate executives wish to build a model to predict the selling price of single family homes. For 319 recently sold homes they have data on the selling price in dollars, square footage, age in years, number of bedrooms, number of bathrooms, and number of garages. The Excel output of a multiple regression with selling price as the response variable and the other variables as the explanatory variables is shown below. Regression Statistics Multiple R 0.9034 R Square 0.8161 Adjusted R Square 0.8131 Standard Error 27350.2517 Observations 319 ANOVA df SS MS Significance F Regression 5 1.0389E+12 2.07779E+11 277.8 0.0000 Residual 313 2.34135E+11 748036266.8 Total 318 1.27303E+12 Coefficients Standard Error t Stat P-value Intercept 31 127.6 9539.67 3.26 0.001224 Sq. Feet 63.1 4.02 15.70 2.26E-41 Age -1144.4 112.78 -10.15 4.19E-21 Bedrooms -8410.4 3002.51 -2.80 0.00541 Bathrooms 3522.0 1581.00 2.23 0.026612 Garage # 28203.5 2858.69 9.87 3.62E-20 24. The proportion of the variation in selling price explained by square footage, age, number of bedrooms, number of bathrooms, and number of garages is: a. 0.8161 b. 277.8 c. 0.0012 d. 0.9034 C. 215 Answer 25. The estimated selling price of a 2,100 square foot home that is 15 years old with 4 bedrooms, 3 bathrooms, and 2 garages is a. $27,350 b. $179,803 c. $281.418 d. $42,144 c. $53,361 Answer 26. For houses with the same square footage, number of bedrooms, number of bathrooms, and number of garages, a 1-year increase in the age of the house results on average in a. a decrease in the selling price of $27.350.25 b. a decrease in the selling price of $1,144.40 c. a decrease in the selling price of $31,127.60 d. an increase in the selling price of $31,127.60 c. an increase in the selling price of $1,144.40