Like many parts ofNorth America, the housing market in Halifax RegionalMunicipality (HRM) has been affectedby COVID-19 and other global factors. In realestate sales, the house is listed for sale at a certainprice (theAsking Price, indollars), and interested buyers will make offers on the house based on this Asking Price. If the demand for housesis low, buyers will typically offer less than theAsking Price; if the demand for houses ishigh (as it is inHRM now), buyers will typically offer more than the Asking Price. The final price a house sells for is the Selling Price(in dollars).

A mathematics professor is trying to buy a house in HRM. Because many houses sell for more than theAsking Price, the professor wants to use statistics to inform the price they offer onhouses. (The professor wants to offer enough money to win the house but not pay much more than other buyers were willingto offer.) Using a dataset provided by a realtor of comparablehouses, the professor conducts a regression analysis to explain the Selling Price(Y) using the Asking Price(X) of the house. An illustration of this is shown below.

700000 600000 500000 SellingPrice 400000 300000 200000- 200000 300000 400000 500000 600000 700000 AskingPriceSuppose that a house that recently sold in HRM has the following values: Variable Value SellingPrice $315000 AskingPrice $330000 1) What is the fitted value for this house? dollars [Round your answer to one (1) decimal place.] 2) What is the residual value for this house? dollars [Round your answer to one (1) decimal place.] 3) Which of the following is the best interpretation of the residual for this house? O A. The house sold for LESS money than it was expected to sell for. The point for this house would be BELOW the regression line. O B. We cannot determine whether the point would fall above or below the regression line based on ONLY the residual. O C. The house sold for MORE money than it was expected to sell for. The point for this house would be ABOVE the regression line. O D. The house sold for MORE money than it was expected to sell for. The point for this house would be BELOW the regression line. O E. The house sold for LESS money than it was expected to sell for. The point for this house would be ABOVE the regression line. 4) Which of the following is the best interpretation of the SLOPE? O A. For every 1 dollar increase in the Asking Price of a house, we expect the Selling Price to increase by 74936.9 dollars. O B. For every 74936.9 dollar increase in the Asking Price of a house, we expect the Selling Price to increase by 0.887 dollars. O C. For every 74936.9 dollar increase in the Selling Price of a house, we expect the Asking Price to increase by 0.887 dollars. O D. For every 0.887 dollar increase in the Selling Price of a house, we expect the Asking Price to increase by 74936.9 dollars.5) Which of the following is the best interpretation of the Y-INTERCEPT? OA. 03. oc. on. 05. We cannot meaningfully interpret the value of the Y-intercept because X=0 would be extrapolation for these data. When the value of the X-variable is 74936.9, we would expect the value of the Y-van'able to be 0. When the value of the X-variable is 74936.9, we would expect the value of the Y-variable to be 0.887. When the value of the X-variable is 0.887, we would expect the value of the Yvariable to be 74936.9. When the value of the X-variable is 0, we would expect the value of the Y-variable to be 74936.9. 6) The value of R2 for this regression model is 85.6%. Which of the following is the best interpretation of this value? OA. OB. 00. OD. OE. OF. 06. The regression model using Selling Price as an explanatory variable accounts for 92.5% of the variability in the Asking Price of houses. The regression model using Selling Price as an explanatory variable accounts for 85.6% of the variability in the Asking Price of houses. The regression model using Asking Price as an explanatory variable accounts for 85.6% of the variability in the Selling Price of houses. The regression model using Selling Price as an explanatory variable accounts for 73.3% of the variability in the Asking Price of houses. The regression model using Asking Price as an explanatory variable accounts for 73.3% of the variability in the Selling Price of houses. The regression model using Asking Price as an explanatory variable accounts for 92.5% of the variability in the Selling Price of houses. There is a strong, positive, linear relationship between Asking Price and Selling Price of houses in HRM