:_ : In a study of housing demand, a county assessor is interested in developing a regression model to estimate the selling price of residential properties within herjurisdiotion. She randomly selects several houses and records the selling price in addition to the following values: the size of the house (in square feet), the total 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 le P10726.xlsx. a. Estimate and interpret a multiple regression equation that includes the four potential explanatory variables. Round your answers to one decimal place, it necessary. If your answer is negative number, enter "minus" sign. Selling Price =. I 0 + I I 0 Size + I I 0 Number Rooms +I I 0 Age + I I 0 Attached Garage How do you interpret the coefcient of the Attached Garage variable? If the home has an attached garage and all else G, the selling price 9 by $I I 8 b. Evaluate the estimated regression eguation's goodness of fit. Fits Well v 9 1:. Use the estimated equation to predict the sales price of a 3000-square-foot, 20-yearold home that has seven rooms but no attached garage. Round your answer to the nearest whole dollar, if necessary. $l l 0