Stock prices over a period of fifty (50) years would most likely exhibit no cyclical component….
QM 670 Final Exam
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Stock prices over a period of fifty (50) years would most likely exhibit no cyclical component….
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 Stock prices over a period of fifty (50) years would most likely exhibit no cyclical component.
 True
 False
 On the plot labeled “a”, which of the following is correct?
 There is a trend present.
 There is a linear relationship.
 There is an obvious outlier.
 There is a negative relationship.
 On the plot labeled “b”, there is an outlier present.
 True
 False
 On the plot labeled “c”, which of the following models is most appropriate?
 singleparameter exponential smoothing
 regression
 regression with seasonality (classical timeseries)
 none of the above are appropriate
 In a simple linear regression, we are using monthly advertising expenditures (in $000) to predict monthly profits (in $000). If the least squares equation is y = 21.5 – .1x and the coefficient of determination is .49, the correlation coefficient = ______.
 0.70
 0.70
 unable to be determined from the data.
 In a simple linear regression, we are using monthly advertising expenditures (in $000) to predict monthly profits (in $000). If the least squares equation is y = 21.5 – .1x and the coefficient of determination is .49. The predicted profit = __________ when advertising expenses are $0.
 21.5
 0.1
 $21,500
 none of the above.
 If the correlation coefficient is zero, there is no relationship between x and y.
 True
 False
 Kelvin Shoe Stores carries a basic black dress shoe for men that sells at a rate of 500 each quarter. Their current policy is to order 500 per quarter, with a fixed cost of $30/order. The annual holding cost is 20% of the cost of items held. The following cost structure is applicable:
Order Quantity  Price/pair 
099  $36 
100199  32 
200299  30 
300+  28 
For a price of $36, the optimal order quantity is
 129
 infeasible for this cost structure.
 neither of the above.
 both a and b.
 Kelvin Shoe Stores carries a basic black dress shoe for men that sells at a rate of 500 each quarter. Their current policy is to order 500 per quarter, with a fixed cost of $30/order. The annual holding cost is 20% of the cost of items held. The following cost structure is applicable:
Order Quantity  Price/pair 
099  $36 
100199  32 
200299  30 
300+  28 
The optimal order quantity is
 129
 141
 146
 300
 Foster Inc. carries special holiday items, including Happy Angels (HAs). During the season, the demand for HAs is approximately normally distributed, with a mean of 320 and a standard deviation of 30. It costs Foster $5.00 for each HA unless he orders at least 400, at which the price drops to $4.50/HA. The HAs’ retail price is $10. Unsold items will be given to a local hospital, with a disposal cost of $0.05/HA. Mr. Foster estimates that the goodwill cost of each item short is close to $0.25.
 This is a singleperiod inventory problem.
 This is an EOQ problem.
 This is a periodicreview problem.
 None of the above
 Foster Inc. carries special holiday items, including Happy Angels (HAs). During the season, the demand for HAs is approximately normally distributed, with a mean of 320 and a standard deviation of 30. It costs Foster $5.00 for each HA unless he orders at least 400, at which the price drops to $4.50/HA. The HAs’ retail price is $10. Unsold items will be given to a local hospital, with a disposal cost of $0.05/HA. Mr. Foster estimates that the goodwill cost of each item short is close to $0.25. A Christmastree model is appropriate.
 True
 False
 A regular EOQ model is appropriate when demand is seasonal.
 True
 False
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. First, run a model to predict the profit. Select all which apply.
 Radios is a significant predictor.
 TVs is a significant predictor.
 DVDs is a significant predictor.
 The overall model is significant.
 The intercept is positive.
 Severe multicollinearity is present.
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. Next, run a model to predict the cost. Select all which apply.
 Radios is a significant predictor.
 TVs is a significant predictor.
 DVDs is a significant predictor.
 The overall model is significant.
 The intercept is positive.
 Severe multicollinearity is present.
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. Based on the output, which of the following recommendations would be most appropriate?
 We should stock more radios.
 We should stock fewer TVs.
 We should increase floor space, since it is probably constraining our sales ability.
 We should consider the time period.
16. What is the best answer given this information? (3)
Model 1  Model 2  Model 3  
Xvariables  6  4  3 
R^{2}  .9344  .8857  .8761 
Adjusted R^{2}  .9058  .8372  .8497 
MSE  5667.53  6044.05  5844.78 
 Model 1 performs the best in all areas.
 Model 2 performs better than Model 3.
 We would most likely prefer Model 1.
 We would most likely prefer Model 2.
 We would most likely prefer Model 3.
17. The table below features three forecasting models used on the same set of data. Select all that apply.
Model 1  Model 2  Model 3  
Type  Singleparameter Exponential smoothing  2parameter Exponential smoothing  3parameter Exponential smoothing 
MSE  8755.3  4876.2  5945.8 
 There is likely a strong seasonal component present.
 There is likely a trend present.
 There is no random component present.
 There is a cyclical component present.
 A different smoothing constant could affect the MSE for Model 1.
 If we increase the order (setup) cost, the order quantity will _____________ if we hold all other costs constant.
 increase
 decrease
 remain the same as long as there is no shortage cost
 become unstable
 If demand is normally distributed,
 a basic EOQ is appropriate.
 a singleperiod model could not be appropriate.
 we should produce to fill demand, rather than filling it through orders.
 none of the above would be true.
 Which of the following methods may be used to determine future order quantities?
 forecasting
 regression
 inventory models
 all of the above
 Refer to the inventory output for Betsy’s Blue Bonnet Bakery. Here, Betsy is trying to determine the optimal order policy for birthday kits. What is the safety stock?
____________________
 Refer to #21. What is Betsy’s service level if she uses this policy?
____________________
 Refer to #21. If Betsy changes to a lost sales model, the order quantity would be expected to increase.
 True
 False
 It depends on the cost associated with a lost sale.
 Refer to the forecasting output for Betsy’s. This model is appropriate for the type of data.
 True
 False
 Refer to #24. Look at the forecast errors. Which of the following best describes the situation?
 The errors are indicative of what we like to see.
 The errors are randomly distributed.
 The errors are indicative of a problem with the model.
 The errors are indicative of a poor choice of a.
 Refer to #24. What recommendation would you make?
 We should use the model as is.
 We should alter model parameters to improve the fit?
 We should use the model, but use extreme caution in doing so.
 We should eliminate some time periods for forecasting.
Regression Data I


MULTIPERIOD EOQ MODEL (Backordering) – NORMAL LEADTIME DEMAND  
PROBLEM:  Betsy’s Blue Bonnet Bakery  
Parameter Values:  
Mean of Demand Distribution: mu =  1,000  
Stand. Deviation of Demand Distribution: sigma =  100  
Fixed Cost per Order: k =  5,000  
Annual Demand Rate: A =  52,000  
Unit Cost of Procuring an Item: c =  42.00  
Annual Holding Cost per Dollar Value: h =  0.20  
Shortage Cost per Unit: p_{S} =  10.00  
Optimal Values:  
Optimal Order Quantity: Q* =  7,919  
Optimal Reorder Point: r* =  1,114  
Expected Demand: mu =  1,000  
Total Expected Cost: TEC(Q*) =  $ 67,471.24  
Expected Shortages: B(r*) =  6.47  
Probability of Shortage: P[D>r*] =  0.13 
Betsy’s Blue Bonnet Bakery  
a =  0.3  g =  0.5  b =  0.8  
Actual  Trend  Slope  Seasonal  Forecast  Error  
Quarter  t  Sales, Y_{t}  T_{t}  b_{t}  S_{t}  F_{t}  
2003 W  1  36,500  
1988 S  2  43,750  36,500.00  7,250.00  1.20  
1988 S  3  59,920  48,601.00  9,675.50  1.23  
1988 F  4  87,440  67,025.55  14,050.03  1.30  
2004 W  5  102,240  87,424.90  17,224.69  1.17  
1988 S  6  123,420  104,144.98  16,972.38  1.19  125,436.15  (2,016.15) 
1988 S  7  139,610  118,753.37  15,790.39  1.19  149,325.16  (9,715.16) 
1988 F  8  135,380  125,312.56  11,174.79  1.13  175,522.72  (40,142.72) 
2005 W  9  129,470  128,753.89  7,308.06  1.04  159,616.61  (30,146.61) 
1988 S  10  137,570  129,989.43  4,271.80  1.08  161,612.88  (24,042.88) 
1988 S  11  156,630  133,566.44  3,924.41  1.18  159,379.23  (2,749.23) 
1988 F  12  150,980  136,498.26  3,428.11  1.11  154,702.82  (3,722.82) 
2006 W  13  143,340  139,362.57  3,146.21  1.03  145,291.38  (1,951.38) 
1988 S  14  153,360  142,190.68  2,987.16  1.08  154,509.63  (1,149.63) 
1988 S  15  169,730  144,939.30  2,867.89  1.17  170,664.76  (934.76) 
1988 F  16  161,990  147,249.54  2,589.07  1.10  164,053.12  (2,063.12) 
2007 W  17  154,760  149,940.86  2,640.19  1.03  154,408.75  351.25 
1988 S  18  164,780  152,592.38  2,645.85  1.08  164,739.26  40.74 
1988 S  19  186,730  156,466.79  3,260.13  1.19  181,930.65  4,799.35 
1988 F  20  177,880  160,230.59  3,511.97  1.11  176,029.75  1,850.25 
2008 W  21  170,360  164,152.06  3,716.72  1.04  168,951.59  1,408.41 
1988 S  22  178,830  167,190.82  3,377.74  1.07  181,270.26  (2,440.26) 
1988 S  23  195,550  168,732.72  2,459.82  1.16  202,826.81  (7,276.81) 
1988 F  24  187,220  170,501.72  2,114.41  1.10  189,772.64  (2,552.64) 
2009 W  25  163,230  168,070.53  (158.39)  0.98  178,936.82  (15,706.82) 
1988 S  26  162,890  163,137.87  (2,545.53)  1.01  179,944.64  (17,054.64) 
1988 S  27  174,540  157,361.67  (4,160.86)  1.12  187,085.45  (12,545.45) 
1988 F  28  163,130  151,724.53  (4,899.00)  1.08  168,543.79  (5,413.79) 
2010 W  29  144,517.86  
1988 S  30  143,788.09  
1988 S  31  153,515.48  
1988 F  32  142,720.95  
MSE =  175,943,211 
QM 670 Final Exam
 Stock prices over a period of fifty (50) years would most likely exhibit no cyclical component.
 True
 False
 On the plot labeled “a”, which of the following is correct?
 There is a trend present.
 There is a linear relationship.
 There is an obvious outlier.
 There is a negative relationship.
 On the plot labeled “b”, there is an outlier present.
 True
 False
 On the plot labeled “c”, which of the following models is most appropriate?
 singleparameter exponential smoothing
 regression
 regression with seasonality (classical timeseries)
 none of the above are appropriate
 In a simple linear regression, we are using monthly advertising expenditures (in $000) to predict monthly profits (in $000). If the least squares equation is y = 21.5 – .1x and the coefficient of determination is .49, the correlation coefficient = ______.
 0.70
 0.70
 unable to be determined from the data.
 In a simple linear regression, we are using monthly advertising expenditures (in $000) to predict monthly profits (in $000). If the least squares equation is y = 21.5 – .1x and the coefficient of determination is .49. The predicted profit = __________ when advertising expenses are $0.
 21.5
 0.1
 $21,500
 none of the above.
 If the correlation coefficient is zero, there is no relationship between x and y.
 True
 False
 Kelvin Shoe Stores carries a basic black dress shoe for men that sells at a rate of 500 each quarter. Their current policy is to order 500 per quarter, with a fixed cost of $30/order. The annual holding cost is 20% of the cost of items held. The following cost structure is applicable:
Order Quantity  Price/pair 
099  $36 
100199  32 
200299  30 
300+  28 
For a price of $36, the optimal order quantity is
 129
 infeasible for this cost structure.
 neither of the above.
 both a and b.
 Kelvin Shoe Stores carries a basic black dress shoe for men that sells at a rate of 500 each quarter. Their current policy is to order 500 per quarter, with a fixed cost of $30/order. The annual holding cost is 20% of the cost of items held. The following cost structure is applicable:
Order Quantity  Price/pair 
099  $36 
100199  32 
200299  30 
300+  28 
The optimal order quantity is
 129
 141
 146
 300
 Foster Inc. carries special holiday items, including Happy Angels (HAs). During the season, the demand for HAs is approximately normally distributed, with a mean of 320 and a standard deviation of 30. It costs Foster $5.00 for each HA unless he orders at least 400, at which the price drops to $4.50/HA. The HAs’ retail price is $10. Unsold items will be given to a local hospital, with a disposal cost of $0.05/HA. Mr. Foster estimates that the goodwill cost of each item short is close to $0.25.
 This is a singleperiod inventory problem.
 This is an EOQ problem.
 This is a periodicreview problem.
 None of the above
 Foster Inc. carries special holiday items, including Happy Angels (HAs). During the season, the demand for HAs is approximately normally distributed, with a mean of 320 and a standard deviation of 30. It costs Foster $5.00 for each HA unless he orders at least 400, at which the price drops to $4.50/HA. The HAs’ retail price is $10. Unsold items will be given to a local hospital, with a disposal cost of $0.05/HA. Mr. Foster estimates that the goodwill cost of each item short is close to $0.25. A Christmastree model is appropriate.
 True
 False
 A regular EOQ model is appropriate when demand is seasonal.
 True
 False
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. First, run a model to predict the profit. Select all which apply.
 Radios is a significant predictor.
 TVs is a significant predictor.
 DVDs is a significant predictor.
 The overall model is significant.
 The intercept is positive.
 Severe multicollinearity is present.
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. Next, run a model to predict the cost. Select all which apply.
 Radios is a significant predictor.
 TVs is a significant predictor.
 DVDs is a significant predictor.
 The overall model is significant.
 The intercept is positive.
 Severe multicollinearity is present.
 See the attached “Regression Data I”. We are using the number of radios, TVs, and DVD players stocked to predict the profit, revenue, and cost for future periods. Based on the output, which of the following recommendations would be most appropriate?
 We should stock more radios.
 We should stock fewer TVs.
 We should increase floor space, since it is probably constraining our sales ability.
 We should consider the time period.
16. What is the best answer given this information? (3)
Model 1  Model 2  Model 3  
Xvariables  6  4  3 
R^{2}  .9344  .8857  .8761 
Adjusted R^{2}  .9058  .8372  .8497 
MSE  5667.53  6044.05  5844.78 
 Model 1 performs the best in all areas.
 Model 2 performs better than Model 3.
 We would most likely prefer Model 1.
 We would most likely prefer Model 2.
 We would most likely prefer Model 3.
17. The table below features three forecasting models used on the same set of data. Select all that apply.
Model 1  Model 2  Model 3  
Type  Singleparameter Exponential smoothing  2parameter Exponential smoothing  3parameter Exponential smoothing 
MSE  8755.3  4876.2  5945.8 
 There is likely a strong seasonal component present.
 There is likely a trend present.
 There is no random component present.
 There is a cyclical component present.
 A different smoothing constant could affect the MSE for Model 1.
 If we increase the order (setup) cost, the order quantity will _____________ if we hold all other costs constant.
 increase
 decrease
 remain the same as long as there is no shortage cost
 become unstable
 If demand is normally distributed,
 a basic EOQ is appropriate.
 a singleperiod model could not be appropriate.
 we should produce to fill demand, rather than filling it through orders.
 none of the above would be true.
 Which of the following methods may be used to determine future order quantities?
 forecasting
 regression
 inventory models
 all of the above
 Refer to the inventory output for Betsy’s Blue Bonnet Bakery. Here, Betsy is trying to determine the optimal order policy for birthday kits. What is the safety stock?
____________________
 Refer to #21. What is Betsy’s service level if she uses this policy?
____________________
 Refer to #21. If Betsy changes to a lost sales model, the order quantity would be expected to increase.
 True
 False
 It depends on the cost associated with a lost sale.
 Refer to the forecasting output for Betsy’s. This model is appropriate for the type of data.
 True
 False
 Refer to #24. Look at the forecast errors. Which of the following best describes the situation?
 The errors are indicative of what we like to see.
 The errors are randomly distributed.
 The errors are indicative of a problem with the model.
 The errors are indicative of a poor choice of a.
 Refer to #24. What recommendation would you make?
 We should use the model as is.
 We should alter model parameters to improve the fit?
 We should use the model, but use extreme caution in doing so.
 We should eliminate some time periods for forecasting.
Regression Data I


MULTIPERIOD EOQ MODEL (Backordering) – NORMAL LEADTIME DEMAND  
PROBLEM:  Betsy’s Blue Bonnet Bakery  
Parameter Values:  
Mean of Demand Distribution: mu =  1,000  
Stand. Deviation of Demand Distribution: sigma =  100  
Fixed Cost per Order: k =  5,000  
Annual Demand Rate: A =  52,000  
Unit Cost of Procuring an Item: c =  42.00  
Annual Holding Cost per Dollar Value: h =  0.20  
Shortage Cost per Unit: p_{S} =  10.00  
Optimal Values:  
Optimal Order Quantity: Q* =  7,919  
Optimal Reorder Point: r* =  1,114  
Expected Demand: mu =  1,000  
Total Expected Cost: TEC(Q*) =  $ 67,471.24  
Expected Shortages: B(r*) =  6.47  
Probability of Shortage: P[D>r*] =  0.13 
Betsy’s Blue Bonnet Bakery  
a =  0.3  g =  0.5  b =  0.8  
Actual  Trend  Slope  Seasonal  Forecast  Error  
Quarter  t  Sales, Y_{t}  T_{t}  b_{t}  S_{t}  F_{t}  
2003 W  1  36,500  
1988 S  2  43,750  36,500.00  7,250.00  1.20  
1988 S  3  59,920  48,601.00  9,675.50  1.23  
1988 F  4  87,440  67,025.55  14,050.03  1.30  
2004 W  5  102,240  87,424.90  17,224.69  1.17  
1988 S  6  123,420  104,144.98  16,972.38  1.19  125,436.15  (2,016.15) 
1988 S  7  139,610  118,753.37  15,790.39  1.19  149,325.16  (9,715.16) 
1988 F  8  135,380  125,312.56  11,174.79  1.13  175,522.72  (40,142.72) 
2005 W  9  129,470  128,753.89  7,308.06  1.04  159,616.61  (30,146.61) 
1988 S  10  137,570  129,989.43  4,271.80  1.08  161,612.88  (24,042.88) 
1988 S  11  156,630  133,566.44  3,924.41  1.18  159,379.23  (2,749.23) 
1988 F  12  150,980  136,498.26  3,428.11  1.11  154,702.82  (3,722.82) 
2006 W  13  143,340  139,362.57  3,146.21  1.03  145,291.38  (1,951.38) 
1988 S  14  153,360  142,190.68  2,987.16  1.08  154,509.63  (1,149.63) 
1988 S  15  169,730  144,939.30  2,867.89  1.17  170,664.76  (934.76) 
1988 F  16  161,990  147,249.54  2,589.07  1.10  164,053.12  (2,063.12) 
2007 W  17  154,760  149,940.86  2,640.19  1.03  154,408.75  351.25 
1988 S  18  164,780  152,592.38  2,645.85  1.08  164,739.26  40.74 
1988 S  19  186,730  156,466.79  3,260.13  1.19  181,930.65  4,799.35 
1988 F  20  177,880  160,230.59  3,511.97  1.11  176,029.75  1,850.25 
2008 W  21  170,360  164,152.06  3,716.72  1.04  168,951.59  1,408.41 
1988 S  22  178,830  167,190.82  3,377.74  1.07  181,270.26  (2,440.26) 
1988 S  23  195,550  168,732.72  2,459.82  1.16  202,826.81  (7,276.81) 
1988 F  24  187,220  170,501.72  2,114.41  1.10  189,772.64  (2,552.64) 
2009 W  25  163,230  168,070.53  (158.39)  0.98  178,936.82  (15,706.82) 
1988 S  26  162,890  163,137.87  (2,545.53)  1.01  179,944.64  (17,054.64) 
1988 S  27  174,540  157,361.67  (4,160.86)  1.12  187,085.45  (12,545.45) 
1988 F  28  163,130  151,724.53  (4,899.00)  1.08  168,543.79  (5,413.79) 
2010 W  29  144,517.86  
1988 S  30  143,788.09  
1988 S  31  153,515.48  
1988 F  32  142,720.95  
MSE =  175,943,211 
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