Refer to the context of the airline in Exercise 31 part (c). Assume that the estimated model meets the conditions for using the MRM for inference.
(a) Does the estimated multiple regression equation explain statistically significant variation in revenue among these feeder cities?
(b) If this model is used to predict revenue for a feeder city, how accurate would you expect those predictions to be?
Exercise 31
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 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
(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?