An airline developed a regression model to predict revenue from flights that connect "feeder" cities to its hub airport.

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