The manager of a furniture factory that operates a morning and evening shift seven days a week wants to forecast the number of c
hairs its factory workers will produce on a given day and shift. The production manager gathers chair production data from the factory and lists whether the production day was a weekday or a weekend (i.e., Saturday or Sunday), and whether the shift was in the morning or evening. Create a regression model to analyze the relationship between the number of chairs produced, whether a day was a weekday or weekend, and whether the shift was in the morning or evening. Be sure to include the residuals and residual plots in your analysis.
2 answers:
Answer:
<em>Answer will be 477</em>
Explanation:
From the details, note that the manager of a furniture factory that operates a morning and evening shifts seven days a week wants to forecast the number of chairs to be produced on a given day and shift by its factory employees.
<em>Consider Y is the vector reflecting the number of chairs the factory produces for its employees. </em>
The independent variables are:
= 1 Weekday (Mon to Fri)
0 Weekend (Sat and Sun)
= 1 Morning Shift
0 Evening shift.
Observe that the regression line is as follows from the regression output given in the problem:
<em>Ŷ </em>= 405.58 + 70.97 * <em>Weekday </em>+ 47.85 * <em>Shift</em>.
Calculate the number of chairs during the evening shift which will be generated on a Thursday.
For this problem, because it's weekday and
because it's a shift from the evening.
<em>Ŷ </em>= 405.58 + 70.97 * <em>Weekday </em>+ 47.85 * <em>Shift</em>.
= 405.58 + 70.97 * (1) + 47.85 * (0)
= 405.58 + 70.97 + 0
= 476.55
= 477
<em>Thus, the number of chairs that will be generated during the evening shift on Thursday will be 477.</em>
You might be interested in