Refer to problem 26.

1. Using Excel, develop a linear regression model for predicting maintenance cost as a function of age of the trucks. Forecast maintenance cost if the age of the truck is 7 years.

2. Using Excel, develop a linear regression model for predicting maintenance cost as a function of the number of miles driven. Forecast maintenance cost if the number of miles driven is 32,300.

3. Evaluate the €œgoodness of fit€ of the regression equations developed in both part a and part b by computing the values of R², r, and s_yx. Which model is a better fit and why?

4. How do the forecasts developed in parts a and b compare with the one developed in problem 24?

Data from problem 26

James Trott, the manager the fictitious Blue Line Trucking Company, wants to develop a forecasting model to predict the maintenance expenditures on the company€™s trucks. The manager believes that a truck€™s maintenance expenditures are closely related to the age of the trucks and the number of miles driven. He collected the data on these three variables for 10 different trucks, and they are given in the following table:

Maintenance Cost (in U.S. dollars) Age of the Truck (in years) Number of Miles Driven 15,200 1230 2480 22.520 18,945 2320 1080 12,405 1042 4 13,897 17,235 2260 11,648 970 10 35,985 4570 37,790 5780 6,400 350