Question

Johnson Filtration, Inc., provides maintenance service for water-filtration systems throughout southern Florida. Suppose that in addition to information on the number of months since the machine was serviced and whether a mechanical or an electrical repair was necessary, the managers obtained a list showing which repairperson performed the service. The revised data follow Repair Time in Hours 3.0 3.1 Months Since Last Service 2 6 8 3 2 7 Type of Repair Electrical Mechanical Electrical Mechanical Electrical Electrical Mechanical Mechanical Electrical Electrical Repairperson Dave Newton Dave Newton Bob Jones Dave Newton Dave Newton Bob Jones Bob Jones Bob Jones Bob Jones Dave Newton 1.8 3.0 4.8 8 4 6 4.7 a. Develop the estimated regression equation to predict the repair time given the number of months since the last maintenance service, the type of repair, and the repairperson who performed the service. Assume that x2 = 1 if the type of repair is electrical and z3 1 if Dave Newton performed the service. Enter negative value as negative number. Time - bo + bj Months + b2Type + b3Person Please, round your answers to three decimal places. bo b1 b2 b3

Answer 1

We have used MS Excel to for regression analysis. Following are the steps follows:

1. On the Data tab, in the Analysis group, click the Data Analysis button.

2. Select Regression and click OK.

3. In the Regression dialog box, configure the following settings:

• Select the Input Y Range, which is your dependent variable. In our case, it's time.
• Select the Input X Range, i.e. your independent variable. In this example, it's months, type, and person.

If you are building a multiple regression model, select two or more adjacent columns with different independent variables.

• Check the Labels box if there are headers at the top of your X and Y ranges.
• Choose your preferred Output option, a new worksheet in our case.

4. Click OK and observe the regression analysis output created by Excel.

Following is the regression output:

Page Layout Formulas Data File Home Insert Review View XLSTAT Cut Calibri Wrap Text ECopy Format Painter B I A Clipboard Merge & Center Paste Font Alignment 011 1 SUMMARY OUTPUT 2 Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.9422 0.8878 0.8316 0.4239 10 10 ANOVA Significance F MS 12 Regression 13 Residual 14 Total 15 16 17 Intercept 18 Months 19 Type 20 Person 8.526 2.842 15.818 1.078 0.180 9.604 0.003 Coefficients 1.994 0.273 1.064 -0.591 standard Error t stat P-value 0.740 2.6950.036 0.085 3.219 0.018 0.308 3.454 0.014 0.394 -1.500 0.184 Lower 95% Upper 95% 3.805 0.481 1.817 0.373 0.184 0.066 0.310 1.555

From the regression output, we can see that:

b₀ = 1.994

b₁ = 0.273

b₂ = 1.064

b₃ = -0.591