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 eachmaintenance 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 callsare reported in the table below.Repair Timein Hours2.93.04.81.82.74.94.64.84.44.5Months SinceLast Service2683279846Type of Repair RepairpersonElectricalMechanicalElectricalMechanicalElectricalElectricalMechanicalMechanicalElectricalElectricalDave NewtonDave NewtonBob JonesDave NewtonDave NewtonBob JonesBob JonesBob JonesBob JonesDave Newton(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.)y = 3.550 + 0.483x2(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 4.94✔% of the variability in the repair time has been explained by the type of repair. Since this is less than55%, the estimated regression equation did not provide ✓ ✓ 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 Newtonperformed the service. (Round your numerical values to three decimal places.)y = 4.700-1.720x3(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 49.4% of the variability in the repair time has been explained by the repairperson. Since this is at least ✓ ✓55%, the estimated regression equation provided✓✓a good fit for the observed data.

The question is about regression Given : To find : a ) Estimated reg. eq. to predict repair time…

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 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 o are reported in the table below. Repair Time in Hours 2.9 3.0 4.8 1.8 2.7 4.6 4.8 Months Since Last Service 4.5 2 6 8 ✓ 3 2 7 9 8 Type of Repair Repairperson 6 Electrical Mechanical Electrical Mechanical Electrical Electrical Mechanical Mechanical Electrical Electrical Dave Newton Dave Newton Bob Jones Dave Newton Dave Newton Bob Jones Bob Jones Dave Newton (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 (x) 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 electrica (Round your numerical values to three decimal places.) 9- 3.550 +0.483x₂ Bob Jones Bob Jones (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 4.94 ✔% of the variability in the repair time has been explained by the type of repair. Since this is less than 55%, the estimated regression equation [did not provide 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 x₂-0 if Bob Jones performed the service and x-1 if Dave Newt performed the service. (Round your numerical values to three decimal places.) 9-4.700-1.720x (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 49.4 x% of the variability in the repair time has been explained by the repairperson. Since this is at least 55%, the estimated regression equation provided ✔a good fit for the observed data.