Johnson Filtration, Inc., provides maintenance service for...

Question

Johnson Filtration, Inc., provides maintenance service for...

Johnson Filtration, Inc., provides maintenance service for water-filtration systems throughout southern Texas. 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 (𝒚). Repair time is believed to be related to three factors (i.e., independent variables), 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.

1. Using appropriate software (e.g., MS Excel), develop the regression equation
(
) that could be used to measure the impact of three independent variables (
,
,
) on the dependent variable (𝒚).

How do you interpret the intercept coefficient value

?

How do you interpret the slope coefficient values

,
and
?

[Hint: you need to convert the independent variable

to a binary variable where "eletrical" is replaced with a value of 0 and "mechanical" is replaced with a value of 1].

[Hint: you need to convert the independent variable

to a binary variable where "Dave Newton" is replaced with a value of 0 and "Bob Jones" is replaced with a value of 1].

1. Utilizing appropriate software (e.g., MS Excel), use the "p-value" approach to test at the 95% confidence level whether impact the of the number of months since the last maintenance service (
: independent variable) on repair time in hours (𝒚: dependent variable) is statistically significant.

1. Utilizing appropriate software (e.g., MS Excel), use the "p-value" approach to test at the 95% confidence level whether impact the of the repairperson who performed the service (
: independent variable) on repair time in hours (𝒚 dependent variable) is statistically significant.

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