A statistical program is recommended. A company provides maintenance service for water-filtration systems throughout sou

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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.
(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--- less than at least 55%,
the estimated regression
equation ---Select--- provided 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 Newton performed the service. (Round your
numerical values to three decimal
places.)ŷ =
(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 % of the variability in the repair time
has been explained by the repairperson. Since this
is ---Select--- less than at least 55%,
the estimated regression
equation ---Select--- provided did not
provide a good fit for the observed data.