. A large food chain owns a number of pharmacies that operate in a variety of settings. Some are situated in small towns and are open for only 8 hours a day, 5 days per week. Others are located in shopping malls and are open for longer hours. The analysts on the corporate staff would like to develop a model to show how a stores revenues depend on the number of hours that it is open. They have collected the following information from a sample of stores.

Hours of Operation	Average Revenue ($)
40	5958
44	6662
48	6004
48	6011
60	7250
70	8632
72	6964
90	11097
100	9107
168	11498

Use a linear function (e.g., y = ax + b; where a and b are parameters to optimize) to represent the relationship between revenue and operating hours and find the values of the parameters using the nonlinear solver that provide the best fit to the given data. What revenue does your model predict for 120 hours?

Suggest a two-parameter nonlinear model (e.g., y = ax^b; where a and b are parameters to optimize) for the same relationship and find the parameters using the Nonlinear Solver that provide the best fit. What revenue does your model predict for 120 hours? Which if the models in (a) and (b) do you prefer and why?

Solve using Python***

Your solutions for (a) and (b) should contain a detailed spreadsheet model (where the decision variables, parameters, objective function and constraints are identified and explained), as well as answers to the questions posed.