D. Observation Quantity Price Advertising Distance 28 250 11 12 69 400 24 3. 43 450 15 32 550 31 42 575 34 6. 72 375 22 66 375 12 8. 49 450 24 10 70 400 22 11 10 60 375 10 Average 53.10 420.00 20.50 5.70 12 13 14 15 16 Regression Statistics 17 Multiple R R-Square Adjusted R-Square 18 0.89 19 0.79 20 0.69 21 Standard Error 9.18 22 Observations 10.00 23 Analysis of Variance 25 24 df Sum of Squares Mean Square Significance F 26 Regression 3.00 1920.99 640.33 7.59 0.0182 27 Residual 6.00 505.91 84.32 28 Total 9.00 2426.90 29 30 Coefficients Standard Error -Statistic P-Value Lower 95% Upper 95% 31 32 Intercept 135.15 20.65 6.54 -2.41 0.0006 0.0500 0.4296 84.61 185.68 33 Price -0.14 0.06 -0.29 0.00 34 Advertising 0.54 0.64 0.85 -1.02 2.09 35 Distance -5.78 1.26 -4.61 0.0037 -8.86 -2.71
FCI owns 10 apartment buildings in a college town, which it rents exclusively to students. Each apartment building contains 100 rental units, but the owner is having cash flow problems due to an average vacancy rate of nearly 50 percent. The apartments in each building have comparable floor plans, but some buildings are closer to campus than others. The owner of FCI has data from last year on the number of apartments rented, the rental price (in dollars), and the amount spent on advertising (in hundreds of dollars) at each of the 10 apartments. These data, along with the distance (in miles) from each apartment building to campus, are presented in rows 1 through 11 of Table. The owner regressed the quantity demanded of apartments on price, advertising, and distance. The results of the regression are reported in rows 16 through 35 of Table. What is the estimated demand function for FCI’s rental units? If FCI raised rents at one complex by $100, what would you expect to happen to the number of units rented? If FCI raised rents at an average apartment building, what would happen to FCI’s total revenues? What inferences should be drawn from this analysis?
Trending now
This is a popular solution!
Step by step
Solved in 3 steps with 5 images
