In a study of housing demand, the county assessor is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor feels that the most important variable affecting selling price (measured in thousands of dollars) is the size of house (measured in hundreds of square feet). She randomly selected 15 houses and measured both the selling price and size, as shown in the following table: Observation (i) Selling Price (x $1,000) (Y) Size (x100 square ft)x? 1 265.2 12.0 2 279.6 20.2 3 311.2 27.0 4 328.0 30.0 5 352.0 30.0 6 281.2 21.4 7 288.4 21.6 8 292.8 25.2 9 356.0 37.2 10 263.2 14.4 11 272.4 15.0 12 291.2 22.4 13 299.6 23.9 14 307.6 26.6 15 320.4 30.7 a. Plot the data b. Determine the estimated regression line. Give an economic interpretation of the estimated slob (b) coefficient. c. Determine whether size is a statistically significant variable in estimating selling price d. Calculate the coefficient of determination e. Perform an F-test of the overall significance of the results f. Construct an approximate 98 percent prediction interval for the selling price of a house having an area (size) of 15 (hundred) square feet