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(a) Ignore for now the months since the last maintenance service (x₁) and the repairperson who performed the service. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (x₂). Let x₂ = 0 if the type of repair is mechanical and x₂ = 1 if the type of repair is electrical. (Round your numerical values to three decimal places.) ŷ= (b) Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that % of the variability in the repair time has been explained by the type of repair. Since this is --Select--55%, the estimated regression equation --Select-- ✓a good fit for the observed data. (c) Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Let x3 = 0 if Bob Jones performed the service and x3 = 1 if Dave Newton performed the service. (Round your numerical values to three decimal places.)

(a) Ignore for now the months since the last maintenance service (x₁) and the repairperson who performed the service. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (x₂). Let x₂ = 0 if the type of repair is mechanical and x₂ = 1 if the type of repair is electrical. (Round your numerical values to three decimal places.) ŷ= (b) Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that % of the variability in the repair time has been explained by the type of repair. Since this is --Select--55%, the estimated regression equation --Select-- ✓a good fit for the observed data. (c) Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Let x3 = 0 if Bob Jones performed the service and x3 = 1 if Dave Newton performed the service. (Round your numerical values to three decimal places.)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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A statistical program is recommended. A company provides maintenance service for water-filtration systems throughout southern Florida. Customers contact the company with requests for maintenance service on their water-filtration systems. To estimate the service time and the service cost, the company'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 performed the service. Data for a sample of 10 service calls are reported in the table below. ŷ = Repair Time in Hours 2.9 3.0 ŷ = 4.8 1.8 2.4 4.9 4.7 4.8 4.4 4.5 Months Since Last Service 2 6 8 3 2 7 9 8 4 6 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 (a) Ignore for now the months since the last maintenance service (x₁) and the repairperson who performed the service. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (x₂). Let x₂ = 0 if the type of repair is mechanical and X₂ = 1 if the type of repair is electrical. (Round your numerical values to three decimal places.) Dave Newton (b) Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that % of the variability in the repair time has been explained by the type of repair. Since this is --Select--- 55%, the estimated regression equation ---Select--- (c) Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Let x3 = 0 if Bob Jones performed the service and x3 = 1 if Dave Newton performed the service. (Round your numerical values to three decimal places.) (d) Does the equation that you developed in part (c) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that a good fit for the observed data. % of the variability in the repair time has been explained by the repairperson. Since this is ---Select--- 55%, the estimated regression equation ---Select--- a good fit for the observed data.
Transcribed Image Text:A statistical program is recommended. A company provides maintenance service for water-filtration systems throughout southern Florida. Customers contact the company with requests for maintenance service on their water-filtration systems. To estimate the service time and the service cost, the company'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 performed the service. Data for a sample of 10 service calls are reported in the table below. ŷ = Repair Time in Hours 2.9 3.0 ŷ = 4.8 1.8 2.4 4.9 4.7 4.8 4.4 4.5 Months Since Last Service 2 6 8 3 2 7 9 8 4 6 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 (a) Ignore for now the months since the last maintenance service (x₁) and the repairperson who performed the service. Develop the estimated simple linear regression equation to predict the repair time (y) given the type of repair (x₂). Let x₂ = 0 if the type of repair is mechanical and X₂ = 1 if the type of repair is electrical. (Round your numerical values to three decimal places.) Dave Newton (b) Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that % of the variability in the repair time has been explained by the type of repair. Since this is --Select--- 55%, the estimated regression equation ---Select--- (c) Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Let x3 = 0 if Bob Jones performed the service and x3 = 1 if Dave Newton performed the service. (Round your numerical values to three decimal places.) (d) Does the equation that you developed in part (c) provide a good fit for the observed data? Explain. (Round your answer to two decimal places.) We see that a good fit for the observed data. % of the variability in the repair time has been explained by the repairperson. Since this is ---Select--- 55%, the estimated regression equation ---Select--- a good fit for the observed data.
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