Question

Part B- Short Answer Problems 1) Johnson Filtration, Inc. provides maintenance service for water-filtration systems. To better estimate repairs times for improved scheduling, the company collected data that show the repair time in hours, the months since last service ('Months'), the repair type (either electrical or mechanical) and the repair person (denoted as simply "technician 1" and "technician 2") for a sample of 40 recent maintenance service calls. As both repair type and repair person are categorical variables, both were replaced with dummy variables "Electrical" (1= electrical, 0-not electrical, ie, mechanical) and "Technician 1" (1= technician 1, 0=not technician 1). Use the results of the regression analysis given below to answer the following questions. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.793025671 0.628889714 0.597963857 0.738705658 Observations 40 ANOVA Significance F 7.01317E-08 df MS 20.33540125 Regression 3 33.29023431 11.09674477 0.545686049 Residual 36 19.64469777 52.93493208 Total 39 Upper 95% Lower 95% t Stat P-value Standard Error Coefficients 3.355190246 1.292449861 5.55233E-05 4.569576999 0.508541612 2.323820053 Intercept 0.346972309 0.000436583 0.108457912 3.872534795 0.058802599 0.227715111 Months 1.378388629 0.35530533 3.4367625 0.001501108 0.252227781 0,86684698 Electrical 0.241692598 -1.088473096 0.204905279 -1.291080094 0.327934921 -0.42339025 Technician 1

Answer 1

The regression equation is:

y = a + b121 +62.22 + b3.13

Here y is the repair time, $\alpha$ the intercept, $x_1$ is the time in months, $x_2$ is the indicator variable (1 or 0) regarding the engineer type (1 for electrical and 0 for mechanical), and $x_3$ is the indicator variable regarding the technician.

d) Here

a = 2.323820053, b1 = 0.227715111,62 = 0.86684698,63 = -0.42339025

because the service was mechanical involving technician 1.

21 = 5,12 = 0,13 = 1

Substituting all of this:

                             

3 = 2,323820053+0.227715111:5+0.86684698.0 – 0.42339025. û = 3.039005358

is the estimate based on the regression equation.