Question

1. Chris Smith keeps track quarterly data of sport car sold by local car dealer during...

1. Chris Smith keeps track quarterly data of sport car sold by local car dealer during last 3 years. Download the file from

DataxlsxQ3

and answer the following questions:

A. Create Holt’s method to minimize the MSE for the data set

B. Create Holt-Winter’s additive method to minimize the MSE for the data set

C. Create Holt-Winter’s multiplicative method to minimize the MSE for the data set

D. Use regression analysis to fit an additive seasonal model with linear trend to the data set.

Explain the results (i.e., p-value, R square, and coefficient) (10 points).

DataxlsxQ3

Year

Quarter

Time

Units Sold

2011

1

1

23

2

2

25

3

3

36

4

4

31

2012

1

5

26

2

6

28

3

7

48

4

8

36

2013

1

9

31

2

10

42

3

11

53

4

12

43

2014

1

13

--

2

14

--

3

15

--

4

16

--

2. Oriented Strand Board tries to decide whether they want to produce a new car or not. The company only wants to produce the car if at least 80% chance of having positive NPV over the next ten years. If they introduce the car, they will have to pay the uncertain initial fee (min is $2 billion, max is $2.4 billion, and most likely case is $2.1 billion). First year, they would produce 100,000 units. The demand of first year is uncertain but expected to be normal distribution with mean of 95,000 and standard deviation of 7,000. For any year in which the demand exceeds production, production will be increased by 5% in the following year. For any year in which the product exceeds demand, production will be decreased by 5% in the next year, and the excess cars will be sold to other companies at a 20% discount. After first year, the demand in any year is a normal distribution with a mean equal to the actual demand in previous year and standard deviation of 7,000. In first year, the sales price of the car will be $13,000 and the total variable cost per car is expected to be $9,500. Both the selling price and variable cost is expected to increase each year in the uniform distribution between 2% and 7%. The company uses a discount rate of 9% to discount future cash flows.

A. Create best case, worst case, and most likely case simulation

B. Create a spreadsheet simulation for this problem.

Hint: Use NPV() function to discount the profits Oriented Strand Board would earn each year

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Answer #1

answer:-

10 alpha beta RMSE MSE 7.6657 58.76296 Year Quarter Time Units Sold Level Trend Forecast Error 2011 23 25 36 3131.128 2.3536 26 31.98528 2.054336 33.4816 28 32.83169 1.812751 34.03962 -6.03962 48 37.31556 2.346974 34.64444 13.35556 36 38.93002 2.200472 39.66253 3.66253 31 39.1044 1.795253 41.1305-10.1305 42 41.11972 1.839267 40.89965 1.100351 5344.96719 2.240907 42.95899 10.04101 43 46.36648 2.072583 47.2081-4.2081 25 28.8 27 31.16 2.36 0.16 7.4816 2012 5 2013 12 2014 48.43906 50.51164 52.58423 54.65681x B$3 D11+(1-B$3) (E10+F10) alpha 4 beta 0.2|Tt = β (44.1) + (1-β)Tel RMSE 5 MSE 7.6657 58.76296 Year Quarter Time Units Sold Level Trend Forecast Error 23 25 36 2011 25 28.8 3 2.36 27alpha beta RMSE MSE 0.2|Tt-p (44-1) + (1-β)T-1 7.6657 58.76296 Quarter Time Units Sold Level 23 25 36 Year Trend Forecast Error 2011 25 28.8 2.36 27G11 alpha beta RMSE MSE 0.2|Tt = β (44.1) + (1-B) 1 7.6657 58.76296 Year Quarter Time Units Sold Level Trend Forecast Error 2011 23 25 36 25 28.8 0 2.36 27Query LO All CColumns Get & Transform Connections Solver Parameter B6 在 -8512 3 alpha 4 beta 5 RMSE 6 MSE Set Objective To: OMax By Changing Variable Cells: OMin O Value of 0.2|Tt-p (44-1) + (1-β)Tel 7.6657 58,76296 SBS3:SBS4 Subject to the Constraints: SBS3:SBS41 8 Year Quarter Time Units Sold Lev 23 25 36 31 26 31 28 32 48 37 36 38 31 3 42 41 53 44 43 4 2011 Add Change Delete 2012 Reset All Load/Save 2013 Make Unconstrained Variables Non-Negative Select a Solving Method: GRG Nonlinear Options 12 13 14 2014 Solving Method Select the GRG Nonlinear engine for Solver Problems that are smooth nonlinear. Select the LP Simplex engine for linear Solver Problems, and select the Evolutionary engine for Solver problems that are non-smooth. Holts Method Sheet1 (2) Holt Help Solve Close6 XB5A2 Solver Results alpha Solver found a solution. All Constraints and optimality conditions are satisfied Reports 0.1 6093|Tt= β (노 노1) + (1-β)Tt-1 RMSE MSE 6.884766 47.4 Answer Sensitivity Limits Keep Solver Solution ORestore Original Values Year Quarter Time Units Sold Level 23 25 36 31 26 28 2011 Return to Solver Parameters Dialog □ outline Reports 25 27 29 31 Cancel Save Scenario. 2012 35 37 Solver found a solution. All Constraints and optimality conditions are satisfied. 36 31 42 53 43 When the GRG engine is used, Solver has found at least a local optimal solution. When Simplex LP 2013 is used, this means Solver has found a global optimal solution. 41 43 45 12 13 45 47 49 51 53 2014RMSE MSE 6.884766 47.4 Year Quarter Time Units Sold Level Trend Forecast Error 23 25 36 31 26 28 2011 25 27 29 Scatter Plot for Actual versus Forecast 2 60 2012 35 37 39 2 40 36 31 42 2013 2 20 43 45 10 12 43 2014 10 12 14 16 18 Units SoldF ForecastC. Using Holt-Winter's Multiplicative Method
ㄨ ˇ 左 =D8/AVERAGE(D$8:D$11) alpha beta gamma RMSE 6.322163 S-(Y/L)(1-y)S.M MSE 0.2 Initial Values: Initial Value for Seasonal D10/AVERAGE(D$10:D$13) Initial Value for Level D14/G10 Initial Trend E14-D13/G13 Forecast.> Ft+k= (Lt+k*Tt)*St-M-k 0.5 T,= β (Lt. Lt.1) + (1-β),1 39.96975 Fk (LtkT) SMk Year Quarter Time Units Sold Level Trend Seasonal Forecast Error 2011 23 25 36 31 26 28 48| 38.88907| 3.560213| 1.243227| 48.86984| -0.86984 36 40.63684 .197726 0.982078 45.7714 31 42.81766 2.994343 42 46.77232 3.186407 0.863892 38.015553.984449 53 48.49318 2.893298 1.168082 62.11004 -9.11004 43 49.86612 2.589227 0.922194 50.465547.46554knumber of timestamps ahead to forecast 0.8 0.869565 1.252174 1.078261 0.8 2012 32.5 35.44 3.75 3.588 0.829816 31.52174 3.52174 9.7714 0.762 35.06766 4.06766 2013 12 2014 13 39.97099 47.55255 67.32101 55.53729 16 41 alpha 2 beta 0.2 0.5 Tt= β (Lt. Lt.) + (1-B) 63221 63 St=y(Y/L)+(1-7)St-M 39.96975 Fttr (Lt+k*T)*St-M+k Initial Values Initial Value for Seasonal =D10/AVEF Initial Value for Level D14/G10 Initial Trend =E14-D13/G13 Forecast.> Ft+k= (Lt+k*Tt)*St-M-k gamma 4 RMSE 5 MSE 7 Year Quarter Time Units Sold Level Trend Seasonal Forecast Error 23 25 36 31 26 2011 0.8 0.869565 1.252174 1.078261 0.8 0 2012 32.5 3.751 alpha 2 beta 0.2 0.5 Tt= β (Lt. Lt.) + (1-B) 63221 63 St=y(Y/L)+(1-7)St-M 39.96975 Fttr (Lt+k*T)*St-M+k Initial Values Initial Value for Seasonal =D10/AVEF Initial Value for Level D14/G10 Initial Trend =E14-D13/G13 Forecast.> Ft+k= (Lt+k*Tt)*St-M-k gamma 4 RMSE 5 MSE 7 Year Quarter Time Units Sold Level Trend Seasonal Forecast Error 23 25 36 31 26 2011 0.8 0.869565 1.252174 1.078261 0.8 0 2012 32.5 3.75F12 alpha 0.2 Initial Values: Initial Value for Seasonal =D10/AVERAGE(D$10:D$13) Initial Value for Level D14/G10 Initial Trend E14-D13/G13 Forecast> Ft+k- (Lt+k Tt) St-M+k beta gamma 0.5 Tt-p (Lt. Lt-1) + (1-β)T,-1 4 RMSE 16.322163 S,=y(Y/L)+ (1-2s,M MSE 39.96975 Fttr (Lt+k*T)*St-M+k 7 Year Quarter Time Units Sold Level Trend Seasonal Forecast Error 23 25 36 31 26 2011 0.8 0.869565 1.252174 1.078261 0.8 0 3 2012 32.5 375UM ﹀ f =BS|1*(D15/G9)+(1.BS1)*(E1 2+F1 2) 0.2 alpha beta gamma RMSE MSE Initial Values Initial Value for Seasonal D1O/AVERAGE(D$10:D$13) Initial Value for Level = D14/G10 Initial Trend-E14-D13/G13 Forecast- Ft+k-(Lt+k Tt) St-M+k 0.5 T, β (Lt-ha) + (1-β)T,-1 | 6322163 S,=y(Y/L)+ (1-2s,M 39.96975 (L+k TSM Quarter Time Seasonal Forecast Error Units Sold Level 23 25 36 31 Year Trend 2011 0.8 0.869565 1.252174 1.078261 0.8 3.588 0.829816 2012 32.5 5751 28-B$1 (D13 31.521743.52174 48 38.88907 3.560213.243227 48.86984 0.86984 36 40.63684 .197726 0.982078 45.7714 31 42.81766 2.994343 42 46.77232 3.186407 0.863892 38.01555 3.984449 53 48.49318 2.893298 1.168082 62.11004 -9.11004 43 49.86612 2.589227 0.92219450.46554-7.46554 knumber of timestamps ahead to foreca: -9.7714 0.762 35.06766 4.06766 2013 12 2014 39.97099 47.55255 67.32101 55.53729 14 4alpha beta Initial Values: Initial Value for Seasonal D10/AVERAGE(D$10:D$13) Initial Value for Level D14/G10 Initial Trend =E14-D13/G13 Forecast.> Ft+k= (Lt+k*Tt)*St-M+k 0.2 gamma 0.5 T, = β (Lt. Lt.) + (1-β)T,-1 63221 63 St=y(Y/L)+(1-7)St-M 4 RMSE MSE 7 Year Quarter Time Units Sold Level Trend Seasonal Forecast Error 23 25 36 31 26 28 35.443.588 B$3 (D13 31.52174 -3.52174 2011 0.8 0.869565 1.252174 1.078261 0.8 2012 32.5 3.75Get External New Fiter Refresh Text to Advanced Columns taue Lo Recent Sources Alles Query. Recent Souce RafdsSort Solver Parameters Get & Transform Connections Sort & Filter SB57 set Objective: To: O Max Ey Changing Variable Cells: B7 XSUMSQ(115:121)/COUNT(015:121) SBS3:SBS5 3 alpha 4 beta 5 gamma Initial Value for Seasonal D10/AVE Initial Value for Level D14/G10 Initial Trend E14-D13/G13 Forecast-> Fttk-(Lttk Tt St-M+k Subject to the Constraints: Add Change Delete MSE Seasonal Forecast Error 9 Year 10 QuarterTime Units Sold Level Trend 2011 23 25 36 31 26 28 48 38.88907 3.560213 1.243227 48.869840.86984 640.63684 3.197726 0.982078 45.7714 142.81766 2.994343 42 46.77232 3.186407 0.863892 38.015553.984449 53| 48.493 18| 2.895298| 1.168082| 62.11004| -9.11004 43 49.86612 2.589227 0.922194 50.46554-7.46554 0.8 Reset All 12 13 1.252174 1.078261 Load/Save Make Unconstrained Variables Non-Negative 5| 3.75 0.81 35.44 2012 ng GRG Nonlinear 3.588 0.829816 31.52174 3.52174 Options Method: 9.7714 4.06766 Solving Method 0.762 35.06766 Select the GRG Nonlinear engine Simplex engine for linear Solver Problems, and select the Evolutionary engine for Solver problems that are non-smooth. for Solver Problems that are smooth nonlinear. Select the LP 20 21 2014 39.97099 47.55255 Help Close 23在 =SUMSA(11 5:21)/CO Solver Results Solver found a solution. All Constraints and optimality conditions are satisfied Reports Answer Sensitivity Limits 0.1 98398|누 α(Y/St-M) + (1-ol)(Lt-1 +T,1) alpha beta O Keep Solver Solution O Restore Original Values gamma 0.265651S Y(1-)S.M RMSE MSE 25.62532 Return to Solver Parameters Dialog Outline Reports Year Quarter Time Units Sold Level 23 25 36 31 26 28 35.44649is used, this means Solver has found a global optimal solution. 48 38.38114 36 39.74276 31 40.63727 0.894512 0.79013 32.8835 42 43.1135 2.476278 0.881817 35.23591 6.764093 53 44.94515 1.831598 1.232488 57.0675 4.0675 43 45.75929 0.814139 1.007813 48. 2011 Cancel Save Scenario.. 3 Solver found a solution. All Constraints and optimality conditions are satisfied. 2012 32.5 2When the GRG engine is used, Solver has found at least a local optimal solution. When Simplex LP 2013 1.8835 12 13 14 2948 -5.2948 k- number of timestamps ahead to forecast 2014 36.79907 41.787180 MSE 25.62532 Year Quarter Time Units Sold Level rend Seasonal Forecast Error 23 25 36 2011 0.8 0.869565 1.252174 1.078261 0.8 Scatter Plot for Actual and Forecast 2012 26 32.5 3.75 28 35.44649 2.946488 0.848408 31.52174 48 38.38114 2.9346551.25176 48.07468 36 39.74276 1.361615 1.032453 44.54921 31 40.63727 0.894512 079013 32,883530 243.11355 2.476278 0.881817 35.23591 20 2013 53 44.94515 1.8315981.232488 57.0675 43 45.75929 0.814139 1.007813 48.2948 36.79907 41.78718 59.40802 49.39882 2014 13 14 12 Units Sold Forecast 16

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