Consider the delivery time data in Example 3.1. In Section 4.2.5 noted that these observations were collected in four cities, San Diego, Boston, Austin, and Minneapolis.

Example 3.1 

a. Develop a model that relates delivery time \(y\) to cases \(x_{1}\), distance \(x_{2}\), and the city in which the delivery was made. Estimate the parameters of the model.
b. Is there an indication that delivery site is an important variable?
c. Analyze the residuals from this model. What conclusions can you draw regarding model adequacy?

A soft drink bottler is analyzing the vending machine service routes in his distribu- tion system. He is interested in predicting the amount of time required by the route driver to service the vending machines in an outlet. This service activity includes stocking the machine with beverage products and minor maintenance or housekeep- ing. The industrial engineer responsible for the study has suggested that the two most important variables affecting the delivery time (y) are the number of cases of product stocked (x) and the distance walked by the route driver (x2). The engineer has collected 25 observations on delivery time, which are shown in Table 3.2. (Note that this is an expansion of the data set used in Example 2.9.) We will fit the multiple linear regression model y=Bo+B1x1+B2x2 + to the delivery time data in Table 3.2.