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.

Repair Time
in Hours Months Since
Last Service Type of Repair Repairperson

2.9 2 Electrical Dave Newton

3.0 6 Mechanical Dave Newton

4.8 8 Electrical Bob Jones

1.8 3 Mechanical Dave Newton

2.5 2 Electrical Dave Newton

4.9 7 Electrical Bob Jones

4.6 9 Mechanical Bob Jones

4.8 8 Mechanical Bob Jones

4.4 4 Electrical Bob Jones

4.5 6 Electrical 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 (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 =

A2: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.) (Solve for %)
We see that ______% of the variability in the repair time has been explained by the type of repair.

B)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.) ŷ =

B2)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.)(Solve for %)

We see that _____% of the variability in the repair time has been explained by the repairperson.

Question

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.

Repair Time in Hours	Months Since Last Service	Type of Repair	Repairperson
2.9	2	Electrical	Dave Newton
3.0	6	Mechanical	Dave Newton
4.8	8	Electrical	Bob Jones
1.8	3	Mechanical	Dave Newton
2.5	2	Electrical	Dave Newton
4.9	7	Electrical	Bob Jones
4.6	9	Mechanical	Bob Jones
4.8	8	Mechanical	Bob Jones
4.4	4	Electrical	Bob Jones
4.5	6	Electrical	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 (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 =

A2: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.) (Solve for %)

We see that ______% of the variability in the repair time has been explained by the type of repair.

B)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.) ŷ =

B2)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.)(Solve for %)

We see that _____% of the variability in the repair time has been explained by the repairperson.

Accepted Answer

Hi! Thank you for the question, As per the honor code, we are allowed to answer three sub-parts at a…