In a study of housing demand, a county assessor is interested in developing a regression modei to estimate the selling price of residentiai properties within heriurisdiction. She randomly selects severai houses and records the seliing price in addition to the following vaiues: the size of the house (in square feet), the totai number of rooms in the house, the age of the house, and an indication of whether the house has an attached garage. These data are stored in the ie P10_26.xl5x. a. Estimate and interpret a multiple regression equation that inciudes the four potential explanatory variables Round your answers to one decimal place, if necessary. If your answer is negative number, enter "minus\" sign. Seliing Price : o + 78.4186 9 Size + 7170.14 M Number Rot-ms + -236.29 M Age + -5663.4 M Attached Garage How do you interpret the coefficient of the Attached Garage variable? If the home has an attached garage and all else held constant v| 9, the selling price decreases v| ('0 by 5 0. b. Evaluate the estimated regression eoua on's goodness of fit. | IsWell viw c. Use the estimated equation to predict the sales price ofa BODDrsguareefoot, 207veareoid home that has seven rooms but no attached garage. Round your answerto the nearest whoie dollar, if necessary. 5 236.29 0 House Selling Price Size Number Rooms Age Attached Garage 2 1 $240.800 3070 7 23 3 2 $215.200 2660 6 23 4 3 $199.200 2390 20 5 $182.400 2240 6 9 6 $144.800 1500 7 17 $126.400 1440 8 $312.000 3720 9 31 LO 8 $185.600 2520 15 10 $176.800 2160 8 11 10 $162.400 2140 8 20 12 11 $304.000 3000 8 15 13 12 $256.000 3000 CO 18 14 13 $222.400 2700 7 17 15 14 $159.200 2020 18 16 15 $130.400 1200 6 17 17