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. (Let x₁ represent the number of months since the last maintenance service, x₂ represent the type of repair, x₃ represent the repairperson, and y represent the repair time in hours.)

y^= Find the p-value. (Round your answer to four decimal places.) p-value= State your conclusion. numerical values to four decimal places.) y^= What are the interpretations of the estimated regression parameters? b1 is our best estimate of the change in repair time if we hold repairperson constant and have a 1 month increase in number of months since the last maintenance service .b2 is our best estimate of the change in repair time if we hold the number of months since the last maintenance service constant and the repairperson is Donna Newton and not Bob Jones What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately % of the variation in the values of repair time in the sample. y^= What are the interpretations of the estimated regression parameters? b1 is our best estimate of the change in repair time if we hold type of repair and repairpers the number of months since the last maintenance service and repairperson constant and the type ot repair is electrical and not mechanical the number of months since the last maintenance service and type of repair constant and the repairperson is Donna Newton and not Bob Jo What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately % of the variation in the values of repair time in the sample. (f) Which of these models would you use? Why? We want to select the [ , so the preferred multiple linear regression model is the model i constant and have a 1 month increase in number of months since the last maintenance service .b2 is our best estimate of the change in repair time if we hold .b3 is our best estimate of the change in repair time if we hold What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately K % of the variation in the values of repair time in the sample. Do you see any pattern in the residuals for the two repairpersons? Two of the repairs made by Donna Newton have large residuals and repairs made by Bob Jones typically have residuals. Do these results suggest any potential modifications to your simple linear regression model? The results suggest that using a dummy variable to represent only the repairperson may enhance this fit of the regression model. The results suggest that using a dummy variable to represent only the type of repair may enhance this fit of the regression model. The results suggest that using dummy variables to represent the type of repair and repairperson may enhance this fit of the regression model. The results suggest that the use of dummy variables is not needed. y^= Find the p-value. (Round your answer to four decimal places.) p-value= State your conclusion. numerical values to four decimal places.) y^= What are the interpretations of the estimated regression parameters? b1 is our best estimate of the change in repair time if we hold repairperson constant and have a 1 month increase in number of months since the last maintenance service .b2 is our best estimate of the change in repair time if we hold the number of months since the last maintenance service constant and the repairperson is Donna Newton and not Bob Jones What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately % of the variation in the values of repair time in the sample. y^= What are the interpretations of the estimated regression parameters? b1 is our best estimate of the change in repair time if we hold type of repair and repairpers the number of months since the last maintenance service and repairperson constant and the type ot repair is electrical and not mechanical the number of months since the last maintenance service and type of repair constant and the repairperson is Donna Newton and not Bob Jo What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately % of the variation in the values of repair time in the sample. (f) Which of these models would you use? Why? We want to select the [ , so the preferred multiple linear regression model is the model i constant and have a 1 month increase in number of months since the last maintenance service .b2 is our best estimate of the change in repair time if we hold .b3 is our best estimate of the change in repair time if we hold What does the coefficient of determination tell you about this model? (Round your answer to two decimal places.) This regression model explains approximately K % of the variation in the values of repair time in the sample. Do you see any pattern in the residuals for the two repairpersons? Two of the repairs made by Donna Newton have large residuals and repairs made by Bob Jones typically have residuals. Do these results suggest any potential modifications to your simple linear regression model? The results suggest that using a dummy variable to represent only the repairperson may enhance this fit of the regression model. The results suggest that using a dummy variable to represent only the type of repair may enhance this fit of the regression model. The results suggest that using dummy variables to represent the type of repair and repairperson may enhance this fit of the regression model. The results suggest that the use of dummy variables is not needed