Answer questions 2 and 3 based on the following information. An airline developed a regression model to predict revenue from flights that connect ``feeder'' cities to its hub airport. The dependent variable in the model is the revenue generated by flights operating to a feeder city (in thousands of dollars per month), and the three explanatory variables are the air distance between the hub and the feeder city (in miles), the squared value of the air distance between the hub and the feeder city, and the population of the feeder city (in thousands). The regression model estimates for 37 feeder locations last month is

Estimated Revenue = 87 + 0.3 x Distance - 0.0025 x Distance² + 1.5 x Population,

with an adjusted R² value of 0.73 and a residual standard error of s_e=32.7 (i.e., $32,700).

Question 2 (12.5 points)

The airline plans to expand its operations to add a new feeder city. One possible city has a population of 100K (i.e., 100,000) and is 250 miles from the hub. What is your best estimate for the monthly revenue the airline can generate by offering a flight to this city? (Select the closest answer.)

Question 2 options:

	$74,612
	$155,750
	$468,250
	$655,750

Question 3 (12.5 points)

According to the back-of-the-envelope formula Estimate{"version":"1.1","math":"$\pm$"}2 x s_e, which of the following is the 95% prediction interval for the monthly revenue the airline can generate by offering a flight to this city? (Select the closest answer.)

Question 3 options:

	[$90,350 , $221,150]
	[$123,050 , $188,450]
	[$246,600 , $377,400]
	[$279,300 , $344,700]