Problem: Computer Repair. A computer repair service is examining the time taken on service calls to repair computers. Data are obtained for 30 service calls. The data are in a file named COMPREP5 on the ¿D. Information obtained includes:

\(x_1\) = number of machines to be repaired (NUMBER)

\(x_2\) = years of experience of service person (EXPER)

\(y\) = time taken (in minutes) to provide service (TIME)

Develop a polynomial regression model to predict average time on the service calls using EXPER and NUMBER as explanatory variables. Justify pour model choice including transformations of any variables.

Problem: Instructions. Consider inverse weight.

Mileage and Weight. The variables CITYMPG (y), which is the number of miles per gallon obtained by a car in city driving, and WEIGHT \((x)\), the weight in pounds of the car, are in a file named MPGWT5 on the \(\mathrm{CD}\). This information is available for 147 cars listed in the Road and Track October 2002 issue.

Fit the linear regression using CITYMPG as the dependent variable and WEIGHT as the independent variable.

Examine a scatterplot of these two variables. Can you find a curvilinear model that better describes the relationship between these two variables? If so, what is the regression equation that describes this relationship? Justify your choice of equation.

Problem: Cost Control. Exercise 1 in Chapter 4 discussed data available for a firm that produces corrugated paper for use in making boxes and other packing materials. The variables discussed were

\(y\), total manufacturing cost per month in thousands of dollars (COST)

\(x_{1}\), total production of paper per month in tons (PAPER)

\(x_{2}\), total machine hours used per month (MACHINE)

\(x_{3}\), total variable overhead costs per month in thousands of dollars (OVERHEAD)

\(x_{4}\), total direct labor hours used each month (LABOR)

The data, available in a file named COST8 on the \(\mathrm{CD}\), are monthly and refer to the time period from January 2001 to March 2003. Use the backward elimination procedure to analyze these data, then answer the following questions:

1. What is the regression equation chosen by the backward elimination procedure?
2. What is the \(R^{2}\) for the chosen equation?
3. What is the adjusted \(R^{2}\) for the chosen equation?
4. What is the standard error of the chosen equation?

Problem: I nstructions. Do part (a) only; neglect scatterplots and residual plots;

take inverses of WEIGHT, HP CYLIN, and LITER for your analysis.

2003 Cars. Data on 147 cars were obtained from the October 2002 issue of Road \& Track: The New Cars. The following data are available in a file named CARS 8 on the CD

name of car

weight, in pounds (WEIGHT)

mileage in city driving (CITYMPG) mileage in highway driving (HWYMPG) horsepower, @ \(6300 \mathrm{rpm}\) (HP) number of cylinders (CYLIN) displacement, in liters (LITER)

Using the available data, try to determine what factors involved in the construction of a car affect either mileage in city driving or mileage in highway driving. (Choose either CITYMPG or HWYMPG as your dependent variable. If you choose CITYMPG, do not use HWYMPG as a possible explanatory variable, and vice versa.) Use any of the techniques discussed to select appropriate variables. Be sure to examine scatterplots and residual plots for violations of assumptions and to correct for any such violations.

1. For the model you select, report the estimated regression. Be sure to define the variables used.

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