Question 3 The demand for printers at Courts store in Bukit Timah is indicated below: Table 2: Demand for Printers Week Number of Printers 1 65 2 70 3 54 4 69 5 59 6 70 7 67 Linear regression and exponential smoothing methods were applied to forecast the future demands for the printers. The summary output of the regression analysis is shown in Table 3. SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.138018544 0.019049118 Adjusted R Square -0.177141058 Table 3: Regression Table Standard Error Observations ANOVA 6.67136739 of SS MS F Significance F Regression 1 4.321428571 4.321429 0.097035 0.767917728 Residual 5 222-5357143 44.50714 Total 6 226.8571429 Coefficients Standard Error Stat Intercept X Variable 1 63.28571429 0.392857143 Lower 35% 5.638334535 11.22419 48.79191395 1.26076993 0.311601 0.767918 -2.848055138 P-value 9.8E-05 Upper 95% 77.77951462 3.633709424 Lower 95.0% Upper 95.0% 48.79191395 -2.848055138 77.77951462 3.633769424 (a) (b) Determine the demand forecast from week 2 to week 8 using exponential smoothing and linear regression. The alpha value and the forecast value of the first week are assumed to be is 0.2 and 65 units, respectively. Which forecasting method is better based on the Mean Squared Error (MSE)? Examine whether the linear regression model used for this problem is a good fit or not. (c) On average, there are 20 units of printers in stock per month, and each unit is sold at $80. Generally, the store can sell 15 units per week. What is the inventory turnover per month? The manager of the store thinks that if the inventory turnover is larger than 2, it is good. Do you agree? Provide your reasons.
Question 3 The demand for printers at Courts store in Bukit Timah is indicated below: Table 2: Demand for Printers Week Number of Printers 1 65 2 70 3 54 4 69 5 59 6 70 7 67 Linear regression and exponential smoothing methods were applied to forecast the future demands for the printers. The summary output of the regression analysis is shown in Table 3. SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.138018544 0.019049118 Adjusted R Square -0.177141058 Table 3: Regression Table Standard Error Observations ANOVA 6.67136739 of SS MS F Significance F Regression 1 4.321428571 4.321429 0.097035 0.767917728 Residual 5 222-5357143 44.50714 Total 6 226.8571429 Coefficients Standard Error Stat Intercept X Variable 1 63.28571429 0.392857143 Lower 35% 5.638334535 11.22419 48.79191395 1.26076993 0.311601 0.767918 -2.848055138 P-value 9.8E-05 Upper 95% 77.77951462 3.633709424 Lower 95.0% Upper 95.0% 48.79191395 -2.848055138 77.77951462 3.633769424 (a) (b) Determine the demand forecast from week 2 to week 8 using exponential smoothing and linear regression. The alpha value and the forecast value of the first week are assumed to be is 0.2 and 65 units, respectively. Which forecasting method is better based on the Mean Squared Error (MSE)? Examine whether the linear regression model used for this problem is a good fit or not. (c) On average, there are 20 units of printers in stock per month, and each unit is sold at $80. Generally, the store can sell 15 units per week. What is the inventory turnover per month? The manager of the store thinks that if the inventory turnover is larger than 2, it is good. Do you agree? Provide your reasons.
Cornerstones of Cost Management (Cornerstones Series)
4th Edition
ISBN:9781305970663
Author:Don R. Hansen, Maryanne M. Mowen
Publisher:Don R. Hansen, Maryanne M. Mowen
Chapter8: Budgeting For Planning And Control
Section: Chapter Questions
Problem 41P: Refer to Problem 8.40 for data. Required: 1. Run a multiple regression equation using machine hours,...
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Transcribed Image Text:Question 3
The demand for printers at Courts store in Bukit Timah is indicated below:
Table 2: Demand for Printers
Week
Number of Printers
1
65
2
70
3
54
4
69
5
59
6
70
7
67
Linear regression and exponential smoothing methods were applied to forecast the future
demands for the printers. The summary output of the regression analysis is shown in
Table 3.
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
0.138018544
0.019049118
Adjusted R Square -0.177141058
Table 3: Regression Table
Standard Error
Observations
ANOVA
6.67136739
of
SS
MS
F
Significance F
Regression
1
4.321428571 4.321429 0.097035
0.767917728
Residual
5
222-5357143 44.50714
Total
6
226.8571429
Coefficients Standard Error Stat
Intercept
X Variable 1
63.28571429
0.392857143
Lower 35%
5.638334535 11.22419
48.79191395
1.26076993 0.311601 0.767918 -2.848055138
P-value
9.8E-05
Upper 95%
77.77951462
3.633709424
Lower 95.0% Upper 95.0%
48.79191395
-2.848055138
77.77951462
3.633769424
(a)
(b)
Determine the demand forecast from week 2 to week 8 using exponential
smoothing and linear regression. The alpha value and the forecast value of the
first week are assumed to be is 0.2 and 65 units, respectively.
Which forecasting method is better based on the Mean Squared Error (MSE)?
Examine whether the linear regression model used for this problem is a good fit
or not.
Transcribed Image Text:(c)
On average, there are 20 units of printers in stock per month, and each unit is sold
at $80. Generally, the store can sell 15 units per week. What is the inventory
turnover per month? The manager of the store thinks that if the inventory turnover
is larger than 2, it is good. Do you agree? Provide your reasons.
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