An island ferry company wants to determine what factors impact the revenue it makes from running its

Question:

An island ferry company wants to determine what factors impact the revenue it makes from running its ferry boats to a local island. The passengers are charged to ride the ferry and also for delivering large items to the island, such as automobiles and heavy construction equipment. The data set presented in Table 9.6 consists of the revenue (in dollars), time of day the ferry left the dock, number of passengers on the ferry, number of large objects on the ferry, weather conditions, and number of crew for a random sample of 27 ferry runs over a 1-month period.

a. Using Minitab, run a multiple regression analysis on the total revenue based on time of day, number of passengers, number of large objects, weather conditions, and the number of crew members.

b. Is the overall model useful in predicting revenue?

c. Check any relevant model assumptions and comment on whether you believe such assumptions have been violated.

d. Identify any outliers.

e. Using the results from the regression analysis, determine which predictor(s) have an effect on the amount of revenue generated.

f. Do you think that multicollinearity is a problem?
g. Using a best subsets regression analysis, determine two models that best fits the data according to the three criteria for model selection. Justify why you chose these particular models.
h. Would the ferry operators make more money if they only ran their ferry boats in either the morning or the evening as compared to the afternoon?

Table 9.6