a) A manufacturing company was interested in determining factors associated with the level of output produced at each pl

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a) A manufacturing company was interested in determining factors associated with the level of output produced at each pl

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A A Manufacturing Company Was Interested In Determining Factors Associated With The Level Of Output Produced At Each Pl 1
A A Manufacturing Company Was Interested In Determining Factors Associated With The Level Of Output Produced At Each Pl 1 (345.78 KiB) Viewed 89 times
a) A manufacturing company was interested in determining factors associated with the level of output produced at each plant (output). At each plant, they measured the following: Write down the model to predict level of output produced (output) for product B only based on the above table. b) Explain what is being tested by the productB:shift Night interaction term. Conduct the appropriate hypothesis test at the 5% level of significance for this term. c) mum_employees The number of employees who worked at the plant plant_age The age of the plant in years) productB Product type was either A or B. The variable product is an indicator variable for product B. shiftNight Shift was either "day" or "night”. The variable shiftNight is the indicator variable for the night shift. productB:shiftNight This is an interaction term between product and shift Night In a backwards stepwise regression model using the partial-F (or partial P-value) method, what would the next step be, based on the output above? Explain your answer. d) Consider the two plots below. For each one, answer the following questions: 1) ii) The following is a plot of the data: What model assumptions are being tested in this plot? Based on this plot, comment on whether these model assumptions are met. Output by Number of Employees at a Manufacturing Company's Plants Residuals vs. Fitted 300 200 3000 product . A 100- 0 2500 - shift -100 • Day Night 200 2000 2250 2500 3000 3250 2750 Fitted Values 2000 Normal Q-Q Plot 300 160 180 220 240 260 200 num_employees 200- A multiple regression model produced the following results: 100 5.069 -100- Coefficients: Estimate Std. Error t value (Intercept) 760.4685 150.0178 num_employees 9.1089 0.7985 11.407 plant_age -3.5482 4.4258 -0.802 product 280.8238 68.3550 4.108 shiftnight 100.4470 39.2607 2.558 productB:shiftnight -197.6038 79.3426 -2.491 Pr>t1
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