3. A real estate company developed a linear regression model to explain sales price (SALE, in $) in its market. The model uses three independent variables: Xi=appraised land value of the property (in $); X2=appraised value of the improvements (home value) (in $); and X3=area of living space on the property (in square feet). A regression model SALE = Bo+B1*X1+ B2*X2+ B3*X3+ was run for the recent 1,187 real estate transactions, and the result appears below. For each coefficient, the table shows the coefficient's estimate, its t-stat (between brackets) and its standard error (between parentheses). Int. X1 X2 X; #obs R? 1,187 82.2% 1470.200 [0.26] (5746.782) 0.814 [1.60] (0.510) 0.820 [3.89] (0.211) 13.520 [2.89] (4.680) a) Can you give an interpretation for the coefficient Bo? Does it make sense? b) Compute the predicted sales price for a house that has its land appraised at $39,000, its construction (home value) appraised at $149,000, and with a living area of 1,950 square feet c) Your friend has a construction company and she usually argues with her clients that home improvements pay off, in the sense that each dollar invested in the home value (what appears as value of improvements) translates into a dollar more in sale price. Is her claim true given the model's results? d) If you are thinking of expanding your property with an additional unit that would increase the living area by 100 square feet, what is the predicted increase in sale price of your house? Assume no other characteristic of the house will change with the expansion project. e) Discuss the explanatory power of the model