(c) What is the interpretation of the partial slope for Distance?

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An airline developed a regression model to predict revenue from flights that connect "feeder" cities to its hub airport. The response in the model is the revenue generated by flights operating to the
feeder cities (in thousands of dollars per month), and the two explanatory variables are the air distance between the hub and 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 = 81 +0.3Distance + 1.4Population with R² = 0.75 and se = 31.2. Complete
parts a through d.
(a) The airline plans to expand its operations to add an additional feeder city. The first possible city has population 150,000 and is 275 miles from the hub. A second possible city has population
180,000 and is 250 miles from the hub. Which would you recommend if the airline wants to increase total revenue?
The first city
The second city
(b) What is the interpretation of the intercept in this equation?
A. The intercept estimates fixed revenue based upon the distance between the hub and feeder city.
B. The intercept estimates fixed revenue based upon the population of the feeder city.
C. The intercept estimates fixed revenue regardless of distance or population, such as earnings from air freight.
D. An interpretation of the intercept cannot be determined given the data.
(c) What is the interpretation of the partial slope for Distance?
A. Among comparably populated cites, flights to those that are 100 miles away produce $8,100,000 more revenue per month, on average.
B. Among comparably populated cities, flights to those that are 100 miles away produce $140,000 more revenue per month, on average.
C. Among comparably populated cities, flights to those that are 100 miles away produce $30,000 more revenue per month, on average.
D. An interpretation of the partial slope for Distance cannot be determined given the data.
Transcribed Image Text:An airline developed a regression model to predict revenue from flights that connect "feeder" cities to its hub airport. The response in the model is the revenue generated by flights operating to the feeder cities (in thousands of dollars per month), and the two explanatory variables are the air distance between the hub and 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 = 81 +0.3Distance + 1.4Population with R² = 0.75 and se = 31.2. Complete parts a through d. (a) The airline plans to expand its operations to add an additional feeder city. The first possible city has population 150,000 and is 275 miles from the hub. A second possible city has population 180,000 and is 250 miles from the hub. Which would you recommend if the airline wants to increase total revenue? The first city The second city (b) What is the interpretation of the intercept in this equation? A. The intercept estimates fixed revenue based upon the distance between the hub and feeder city. B. The intercept estimates fixed revenue based upon the population of the feeder city. C. The intercept estimates fixed revenue regardless of distance or population, such as earnings from air freight. D. An interpretation of the intercept cannot be determined given the data. (c) What is the interpretation of the partial slope for Distance? A. Among comparably populated cites, flights to those that are 100 miles away produce $8,100,000 more revenue per month, on average. B. Among comparably populated cities, flights to those that are 100 miles away produce $140,000 more revenue per month, on average. C. Among comparably populated cities, flights to those that are 100 miles away produce $30,000 more revenue per month, on average. D. An interpretation of the partial slope for Distance cannot be determined given the data.
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