# Question: An airline developed a regression model to predict revenue from

An airline developed a regression model to predict revenue from fights that connect “feeder” cities to its hub airport. The response in the model is the revenue generated by fights operating to the feeder cities (in thousands of dollars per month), and the two explanatory variables are the air distance between the hub and the feeder city (Distance, in miles) and the population of the feeder city (in thousands). The least squares regression equation based on data for 37 feeder locations last month is
Estimated Revenue = 87 + 0.3 Distance + 1.5 Population
with R2 = 0.74 and se = 32.7.
(a) The airline plans to expand its operations to add an additional feeder city. One possible city has population 100,000 and is 250 miles from the hub. A second possible city has population 75,000 and is 200 miles from the hub. Which would you recommend if the airline wants to increase total revenue?
(b) What is the interpretation of the intercept in this equation?
(c) What is the interpretation of the partial slope for Distance?
(d) What is the interpretation of the partial slope for Population?

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