3. The data set for this problem was collected from home sales in 3101!!ng Rock, Hort! Carolina {which is located in the mountains of NC). The price that people are willing to pa] for a house is determined by a variety,r of characteristics of the house and available amenitiet {e.g., closeness to schools or shopping). The provided data set contains some of thest variables. Using the data set in the spreadsheet, estimate the following model and answe the following questions: [20) Price = [lo + t'Bedmoms + 2*Fullbath + Bs"I-Ialfbath + B4*Miles + [15'5th + 'Garage + Bar'Acres + a'ew + 39*Days The variables are dened as follows: Price = the price at which the house sold in veart {in dollars) Bedrooms = the number of bedrooms in the house Fullbath = the number of full bathrooms in the house Halfbath = the number of half bathrooms in the house Miles = the number of miles to the nearest shopping center Sqft = the number of square feet in the house Garage = the number of cars that can be parked in the garage Acres = the number of acres of property that comes with the house View = the quality of the view of the mountains form the house {1 = good view and = poor or no view) Days = the number of days the house was on the market before it was sold 1. Which variables are signicant at a critical value of +f- 2w. 2. How much of the variation in the sale price of the house is explained by this model? 3. Interpret the coefficient estimate on Bedroom. 4. Interpret the coefficient estimate on 1view. 5. Forecast the price of a house if the independent variables take the following values: Bedrooms = 3.2 Sqft. = 2,550 Miles = 5.5 Garage = 1.8 Days = 235 View = 1 Fullbath = 2.3 Halfbath = 0.8 Acres = 2.4