One of the company’s production managers is interested in examining
the relationship between the dollars spent per month in inspecting
finished motor products (X) and the number of motors produced
during that month that were returned by dissatisfied customers (Y).
He has collected the data in the file P11_32.xlsx to explore this
relationship for the past 36 months. a. Estimate a simple linear
regression equation using the given data. If required, round your
answers to three decimal places. For subtractive or negative
numbers use a minus sign even if there is a + sign before the
blank. (Example: -300) The predicted number of motors returned when
the inspection expenditures is $0: 66 The predicted change in
number of motors returned that is associated with an increase of
$1,000 in inspection expenditures: 67 b. Examine the residuals of
the regression equation. Do you see evidence of any violations of
the regression assumptions? No c. Conduct a Durbin–Watson test on
the model’s residuals. Interpret the result of this test. There is
some evidence of positive autocorrelation, but it is not
statistically significant. d. In light of your result in part c, do
you recommend modifying the original regression model? Yes
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