Johnson Filtration, Inc., provides maintenance service for water filtration syst
ID: 3354958 • Letter: J
Question
Johnson Filtration, Inc., provides maintenance service for water filtration systems throughout southern Florida. Customers contact Johnson with requests for maintenance service on their water filtration systems. To estimate the service time and the service cost, Johnson's managers want to predict the repair time necessary for each maintenance request. Hence, repair time in hours is the dependent variable. Repair time is believed to be related to three factors; the number of months since the last maintenance service, the type of repair problem (mechanical or electrical), and the repairperson who performs the repair (Donna Newton or Bob Jones). Data for a sample of 10 service calls are reported in the following table.
Click on the datafile logo to reference the data.
Repair Time inHours Months Since Last
Service
Type of Repair
Repairperson 2.9 2 Electrical Donna Newton 3.0 6 Mechanical Donna Newton 4.8 8 Electrical Bob Jones 1.8 3 Mechanical Donna Newton 2.9 2 Electrical Donna Newton 4.9 7 Electrical Bob Jones 4.2 9 Mechanical Bob Jones 4.8 8 Mechanical Bob Jones 4.4 4 Electrical Bob Jones 4.5 6 Electrical Donna Newton
Explanation / Answer
Using R
So the model is
Y= 2.1473 + 0.3041 *X
Call:
lm(formula = Y ~ X)
Residuals:
Min 1Q Median 3Q Max
-1.2597 -0.4772 0.1821 0.4509 1.0362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1473 0.6050 3.549 0.00752 **
X 0.3041 0.1004 3.029 0.01634 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.781 on 8 degrees of freedom
Multiple R-squared: 0.5342, Adjusted R-squared: 0.4759
F-statistic: 9.174 on 1 and 8 DF, p-value: 0.01634
1) So the model is
Y= 2.1473 + 0.3041 *X
2) What are the interpretations of the estimated regression parameters?
If X=0 then value of Y is 2.1473 .
and If X increses 1 unit then then Y increases 0.3041 unit.
------> 0.5342
Interpret the coefficient of determination.
Coefficient of determiantion if 53% variation explian by the responce variable Repair Time in
Hours. this is used for model good or not .
We will give you only 4-bit solutio because of Chegg Rule
a) Use the data to develop the simple linear regression equation to predict repair time given the number of months since the last maintenance service. Let x represent the number of months since the last maintenance service.So the model is
Y= 2.1473 + 0.3041 *X
Call:
lm(formula = Y ~ X)
Residuals:
Min 1Q Median 3Q Max
-1.2597 -0.4772 0.1821 0.4509 1.0362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1473 0.6050 3.549 0.00752 **
X 0.3041 0.1004 3.029 0.01634 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.781 on 8 degrees of freedom
Multiple R-squared: 0.5342, Adjusted R-squared: 0.4759
F-statistic: 9.174 on 1 and 8 DF, p-value: 0.01634
1) So the model is
Y= 2.1473 + 0.3041 *X
2) What are the interpretations of the estimated regression parameters?
If X=0 then value of Y is 2.1473 .
and If X increses 1 unit then then Y increases 0.3041 unit.
What is the coefficient of determination?------> 0.5342
Interpret the coefficient of determination.
Coefficient of determiantion if 53% variation explian by the responce variable Repair Time in
Hours. this is used for model good or not .
We will give you only 4-bit solutio because of Chegg Rule
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