49
categories. Indicate whether each category appears to be stationary or nonstationary. category. Use Solver to estimate the values of α and β that minimize the MSE. What are the optimal values of α and β and the MSE for each model? What is the forecast for next year for each expenditure category? category. What is the estimated regression equation and MSE for each model? What is the forecast for next year for each expenditure category? wants you to estimate the growth rate represented by g in the following equation: Ŷt+1 = Yt(1 + g). That is, the estimated value for time period t + 1 is equal to the actual value in the previous time period (t) multiplied by one plus the growth rate g. Use Solver to identify the optimal (minimum MSE) growth rate for each expenditure category. What is the growth rate for each category? What is the forecast for next year for each expenditure category? would you recommend that Fysco use for each expenditure category? home” has been growing steadily over the past 22 years as shown in the following table (and the file FyscoFoods.xls on your data disk). This table breaks the total expenditures on food away from home (shown in the final column) into six component parts (e.g., eating & drinking places, hotels & motels, etc). generates forecasts of the total market demand in each of the six food away from home expenditure categories. This helps the company allocate its marketing resources among the various customers represented in each category.

Ragsdale Fysco Food Decision Modeling Case

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a. Prepare line graphs of each of the six expenditure categories. Indicate whether each category appears to be stationary or nonstationary.b. Use Holt’s method to create models for each expenditure category. Use Solver to estimate the values of α and β that minimize the MSE. What are the optimal values of α and β and the MSE for each model? What is the forecast for next year for each expenditure category?c. Estimate linear regression models for each expenditure category. What is the estimated regression equation and MSE for each model? What is the forecast for next year for each expenditure category?d. Fysco’s Vice President of Marketing has a new idea for forecasting market demand. For each expenditure category, she wants you to estimate the growth rate represented by g in the following equation: Ŷt+1 = Yt(1 + g). That is, the estimated value for time period t + 1 is equal to the actual value in the previous time period (t) multiplied by one plus the growth rate g. Use Solver to identify the optimal (minimum MSE) growth rate for each expenditure category. What is the growth rate for each category? What is the forecast for next year for each expenditure category?e. Which of the three forecasting techniques considered here would you recommend that Fysco use for each expenditure category?

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Page 1: Ragsdale Fysco Food Decision Modeling Case

a. Prepare line graphs of each of the six expenditure categories. Indicate whether each category appears to be stationary or nonstationary.

b. Use Holt’s method to create models for each expenditure category. Use Solver to estimate the values of α and β that minimize the MSE. What are the optimal values of α and β and the MSE for each model? What is the forecast for next year for each expenditure category?

c. Estimate linear regression models for each expenditure category. What is the estimated regression equation and MSE for each model? What is the forecast for next year for each expenditure category?

d. Fysco’s Vice President of Marketing has a new idea for forecasting market demand. For each expenditure category, she wants you to estimate the growth rate represented by g in the following equation: Ŷt+1 = Yt(1 + g). That is, the estimated value for time period t + 1 is equal to the actual value in the previous time period (t) multiplied by one plus the growth rate g. Use Solver to identify the optimal (minimum MSE) growth rate for each expenditure category. What is the growth rate for each category? What is the forecast for next year for each expenditure category?

e. Which of the three forecasting techniques considered here would you recommend that Fysco use for each expenditure category?

Fysco Foods, Inc. is one of the largest suppliers of institutional and commercial food products in the United States. Fortunately for Fysco, the demand for “food away from home” has been growing steadily over the past 22 years as shown in the following table (and the file FyscoFoods.xls on your data disk). This table breaks the total expenditures on food away from home (shown in the final column) into six component parts (e.g., eating & drinking places, hotels & motels, etc).

As part of its strategic planning process, each year Fsyco generates forecasts of the total market demand in each of the six food away from home expenditure categories. This helps the company allocate its marketing resources among the various customers represented in each category.

Page 2: Ragsdale Fysco Food Decision Modeling Case
Page 3: Ragsdale Fysco Food Decision Modeling Case
Page 4: Ragsdale Fysco Food Decision Modeling Case
Page 5: Ragsdale Fysco Food Decision Modeling Case

(a) See the Next sheets for the explaination. All six series are nonstationary.

(b) Series a b MSE

1 0.491 39171511.4Hotels & motels 0.758 0.698 157086.1

1 0.059 940813

Recreational places 0.852 0.111 822724Schools & colleges 0.95 0.584 87677.7

1 0.501 492037.9

(c )Series Intercept Slope MSE

-19682495.4 9976.6 48497973.7

Hotels & motels -1073108.6 545.2 230616-744887.2 380.9 1439823.2

Recreational places-933269.2 472.4 806761.7

Schools & colleges -1659605.4 843 614900.8-1722215.6 878.1 520190.6

(d) Series Growth rate MSE Forecast

0.0553 34744362.4 306749.8

Hotels & motels 0.0433 149423.1 18675.40.0266 868556.9 17330.7

Recreational places0.0472 790450.3 12366.7

Schools & colleges 0.0446 69928.3 29263

Eating & drinking places

Retail stores, direct selling

Eating & drinking places

Eating & drinking places

Retail stores, direct selling

Eating & drinking places

Eating & drinking places

Retail stores, direct selling

Page 6: Ragsdale Fysco Food Decision Modeling Case

0.0339 382278.3 36191.9

(e) For each series, the growth rate approach had the smallest MSE. The moral of the case is that sometimes the simplest forecasting method is the best!

Eating & drinking places

Page 7: Ragsdale Fysco Food Decision Modeling Case

Forecast

30487918599.5

17153.7

12155.328956.3

35827.8

Forecast

290609.6

18400.817578.7

12501.2

27992.435652.3

Page 8: Ragsdale Fysco Food Decision Modeling Case

For each series, the growth rate approach had the smallest MSE. The moral of the case is that sometimes the simplest forecasting method is the best!

Page 9: Ragsdale Fysco Food Decision Modeling Case

Year1 75,883 5,906 8,158 3,040 11,115 16,1942 83,358 6,639 8,830 2,979 11,357 17,7513 90,390 6,888 9,256 2,887 11,692 18,6634 98,710 7,660 9,827 3,271 12,338 19,0775 ### 8,409 10,315 3,489 12,950 20,0476 ### 9,168 10,499 3,737 13,534 20,1337 ### 9,665 11,116 4,059 14,401 20,7558 ### 11,117 12,063 4,331 14,300 21,1229 ### 11,905 13,211 5,144 14,929 22,88710 ### 12,179 14,440 6,151 15,728 24,58111 ### 12,508 16,053 7,316 16,767 26,19812 ### 12,460 16,750 8,079 17,959 27,10813 ### 13,204 13,588 8,602 18,983 27,94614 ### 13,362 13,777 9,275 19,844 28,03115 ### 13,880 14,210 9,791 21,086 28,20816 ### 14,195 14,333 10,574 22,093 28,59717 ### 14,504 14,475 11,354 22,993 28,98118 ### 15,469 14,407 8,290 24,071 30,92619 ### 15,800 15,198 9,750 25,141 31,92620 ### 16,623 16,397 10,400 26,256 33,56021 ### 17,440 16,591 11,177 27,016 34,50822 ### 17,899 16,881 11,809 28,012 35,004

Eating &

drinking

places

Hotels &

motels

Retail stores, direct selling

Recreational

places

Schools &

collegesAll

other

Page 10: Ragsdale Fysco Food Decision Modeling Case

Total##################################################################

Page 11: Ragsdale Fysco Food Decision Modeling Case

1 4 7 10 13 16 19 22

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drink-ing places

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

6,000

8,000

10,000

12,000

14,000

16,000

18,000

Retail stores, direct selling

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

0

5,000

10,000

15,000

20,000

25,000

30,000

Schools & colleges

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Year

Ex

pe

nd

itu

res

Page 12: Ragsdale Fysco Food Decision Modeling Case

1 4 7 10 13 16 19 22

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

0

5,000

10,000

15,000

20,000

25,000

30,000

Schools & colleges

Year

Exp

en

dit

ure

s

1 4 7 10 13 16 19 22

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Year

Ex

pe

nd

itu

res

Page 13: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Holts

Page 13

Year Trend1 75,883 75883 0.00 -- alpha =2 83,358 83358 3673.30 75883.0 beta =3 90,390 90390 5323.81 87031.3

4 98,710 98710 6796.17 95713.8 MSE =5 105,836 105836 6958.25 105506.26 111,760 111760 6450.01 112794.37 121,699 121699 8164.54 118210.08 146,194 146194 16189.52 129863.59 160,855 160855 15438.39 162383.5

10 171,157 171157 12914.31 176293.411 183,484 183484 12625.70 184071.312 188,228 188228 8752.54 196109.713 183,014 183014 1889.21 196980.514 195,835 195835 7261.22 184903.215 205,768 205768 8574.17 203096.216 214,274 214274 8540.67 214342.217 221,735 221735 8010.11 222814.718 235,597 235597 10885.80 229745.119 248,716 248716 11983.22 246482.820 260,495 260495 11882.86 260699.221 275,695 275695 13512.94 272377.922 290,655 290655 14224.04 289207.923 -- -- -- 304879.0

Eating & drinking

placesBase Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

YearE

xp

en

dit

ure

s

Page 14: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Holts

Page 14

10.491

39171511.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

J5
Variable cell
J6
Variable cell
J8
Set cell
Page 15: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Regression

Page 15

Year1 75,883 71,125 intercept slope2 83,358 81,102 61148.4 9976.63 90,390 91,0784 98,710 101,055 MSE =5 105,836 111,0316 111,760 121,0087 121,699 130,9848 146,194 140,9619 160,855 150,938

10 171,157 160,91411 183,484 170,89112 188,228 180,86713 183,014 190,84414 195,835 200,82015 205,768 210,79716 214,274 220,77417 221,735 230,75018 235,597 240,72719 248,716 250,70320 260,495 260,68021 275,695 270,65622 290,655 280,63323 -- 290,610

Eating & drinking

placesPredicted

Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 16: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Regression

Page 16

MSE Forecast48497973.7 290609.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 17: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Growth

Page 17

Year1 75,883 -- g= 0.0553742562 83,358 80085.0

3 90,390 87973.9 MSE = 34744362.44 98,710 95395.35 105,836 104176.06 111,760 111696.67 121,699 117948.68 146,194 128438.09 160,855 154289.4

10 171,157 169762.211 183,484 180634.712 188,228 193644.313 183,014 198651.014 195,835 193148.315 205,768 206679.216 214,274 217162.217 221,735 226139.318 235,597 234013.419 248,716 248643.020 260,495 262488.521 275,695 274919.722 290,655 290961.423 -- 306749.8

Eating & drinking

placesPredicted

Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: F5By Changing: F3

Minimize: F5By Changing: F3

H5
Variable cell
H7
Set cell
Page 18: Ragsdale Fysco Food Decision Modeling Case

Series 1 - Growth

Page 18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Eating & drinking places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: F5By Changing: F3

Minimize: F5By Changing: F3

Page 19: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Holts

Page 19

Year Trend1 5,906 5906 0.00 -- alpha =2 6,639 6461.503 387.92 5906.0 beta =3 6,888 6878.658 408.33 6849.4

4 7,660 7569.676 605.74 7287.0 MSE =5 8,409 8352.437 729.36 8175.46 9,168 9147.125 774.98 9081.87 9,665 9727.258 638.91 9922.18 11,117 10935.19 1036.27 10366.29 11,905 11921.09 1001.10 11971.5

10 12,179 12359 607.79 12922.211 12,508 12619.09 365.01 12966.812 12,460 12586.91 87.65 12984.113 13,204 13075.79 367.84 12674.614 13,362 13381.77 324.64 13443.615 13,880 13838 416.51 13706.416 14,195 14209.4 385.03 14254.517 14,504 14525.9 337.17 14594.418 15,469 15322.27 657.84 14863.119 15,800 15843.62 562.52 15980.120 16,623 16570.49 677.29 16406.121 17,440 17393.45 779.02 17247.822 17,899 17965.22 634.29 18172.523 -- -- -- 18599.5

Hotels & motels

Base Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

YearE

xp

en

dit

ure

s

Page 20: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Holts

Page 20

0.7578485350.698

157086.1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

J5
Variable cell
J6
Variable cell
J8
Set cell
Page 21: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Regression

Page 21

Year1 5,906 6,406 intercept slope2 6,639 6,951 5861.0 545.23 6,888 7,4974 7,660 8,042 MSE =5 8,409 8,5876 9,168 9,1327 9,665 9,6778 11,117 10,2239 11,905 10,768

10 12,179 11,31311 12,508 11,85812 12,460 12,40413 13,204 12,94914 13,362 13,49415 13,880 14,03916 14,195 14,58417 14,504 15,13018 15,469 15,67519 15,800 16,22020 16,623 16,76521 17,440 17,31022 17,899 17,85623 -- 18,401

Hotels & motels

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 22: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Regression

Page 22

MSE Forecast230616.0 18400.8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 23: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Growth

Page 23

Year1 5,906 -- g= 0.043379182 6,639 6162.2

3 6,888 6927.0 MSE = 149423.14 7,660 7186.85 8,409 7992.36 9,168 8773.87 9,665 9565.78 11,117 10084.39 11,905 11599.2

10 12,179 12421.411 12,508 12707.312 12,460 13050.613 13,204 13000.514 13,362 13776.815 13,880 13941.616 14,195 14482.117 14,504 14810.818 15,469 15133.219 15,800 16140.020 16,623 16485.421 17,440 17344.122 17,899 18196.523 -- 18675.4

Hotels & motels

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

Year

Ex

pe

nd

itu

res

H5
Variable cell
H7
Set cell
Page 24: Ragsdale Fysco Food Decision Modeling Case

Series 2 - Growth

Page 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Hotels & motels

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 25: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Holts

Page 25

Year Trend1 8,158 8158 0.00 -- alpha =2 8,830 8830 39.34 8158.0 beta =3 9,256 9256 61.97 8869.3

4 9,827 9827 91.77 9318.0 MSE =5 10,315 10315 114.96 9918.86 10,499 10499 119.01 10430.07 11,116 11116 148.16 10618.08 12,063 12063 194.92 11264.29 13,211 13211 250.71 12257.9

10 14,440 14440 307.98 13461.711 16,053 16053 384.37 14748.012 16,750 16750 402.67 16437.413 13,588 13588 194.00 17152.714 13,777 13777 193.71 13782.015 14,210 14210 207.72 13970.716 14,333 14333 202.76 14417.717 14,475 14475 199.20 14535.818 14,407 14407 183.56 14674.219 15,198 15198 219.12 14590.620 16,397 16397 276.48 15417.121 16,591 16591 271.65 16673.522 16,881 16881 272.73 16862.723 -- -- -- 17153.7

Retail stores, direct selling

Base Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

YearE

xp

en

dit

ure

s

Page 26: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Holts

Page 26

10.059

940813.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

Year

Ex

pe

nd

itu

res

J6
Variable cell
J8
Set cell
Page 27: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Regression

Page 27

Year1 8,158 9,200 intercept slope2 8,830 9,581 8819.1 380.93 9,256 9,9624 9,827 10,342 MSE =5 10,315 10,7236 10,499 11,1047 11,116 11,4858 12,063 11,8669 13,211 12,247

10 14,440 12,62811 16,053 13,00812 16,750 13,38913 13,588 13,77014 13,777 14,15115 14,210 14,53216 14,333 14,91317 14,475 15,29418 14,407 15,67419 15,198 16,05520 16,397 16,43621 16,591 16,81722 16,881 17,19823 -- 17,579

Retail stores, direct selling

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 28: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Regression

Page 28

MSE Forecast1439823.2 17578.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 29: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Growth

Page 29

Year1 8,158 -- g= 0.0266392432 8,830 8375.3

3 9,256 9065.2 MSE = 868556.94 9,827 9502.65 10,315 10088.86 10,499 10589.87 11,116 10778.78 12,063 11412.19 13,211 12384.3

10 14,440 13562.911 16,053 14824.712 16,750 16480.613 13,588 17196.214 13,777 13950.015 14,210 14144.016 14,333 14588.517 14,475 14714.818 14,407 14860.619 15,198 14790.820 16,397 15602.921 16,591 16833.822 16,881 17033.023 -- 17330.7

Retail stores, direct selling

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

Year

Ex

pe

nd

itu

res

H7
Set cell
Page 30: Ragsdale Fysco Food Decision Modeling Case

Series 3 - Growth

Page 30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Retail stores, direct sell-ing

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 31: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Holts

Page 31

Year Trend1 3,040 3040 0.00 -- alpha =2 2,979 2987.994 -5.75 3040.0 beta =3 2,887 2901.042 -14.74 2982.2

4 3,271 3214.282 21.55 2886.3 MSE =5 3,489 3451.675 45.44 3235.86 3,737 3701.632 68.07 3497.17 4,059 4016.347 95.36 3769.78 4,331 4298.669 116.05 4111.79 5,144 5036.478 184.85 4414.7

10 6,151 6013.934 272.56 5221.311 7,316 7164.214 369.69 6286.512 8,079 7998.633 421.11 7533.913 8,602 8575.129 438.31 8419.714 9,275 9236.436 462.98 9013.415 9,791 9777.498 471.62 9699.416 10,574 10526.1 502.27 10249.117 11,354 11306 532.99 11028.418 8,290 8813.246 198.17 11839.019 9,750 9641.107 267.85 9011.420 10,400 10327.6 314.18 9909.021 11,177 11098.09 364.67 10641.822 11,809 11758 397.34 11462.823 -- -- -- 12155.3

Recreational places

Base Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 32: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Holts

Page 32

0.8525646940.111

822724.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

J5
Variable cell
J6
Variable cell
J8
Set cell
Page 33: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Regression

Page 33

Year1 3,040 2,108 intercept slope2 2,979 2,580 1635.7 472.43 2,887 3,0534 3,271 3,525 MSE =5 3,489 3,9986 3,737 4,4707 4,059 4,9438 4,331 5,4159 5,144 5,887

10 6,151 6,36011 7,316 6,83212 8,079 7,30513 8,602 7,77714 9,275 8,24915 9,791 8,72216 10,574 9,19417 11,354 9,66718 8,290 10,13919 9,750 10,61220 10,400 11,08421 11,177 11,55622 11,809 12,02923 -- 12,501

Recreational places

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 34: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Regression

Page 34

MSE Forecast806761.7 12501.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 35: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Growth

Page 35

Year1 3,040 -- g= 0.0472264292 2,979 3183.6

3 2,887 3119.7 MSE = 790450.34 3,271 3023.35 3,489 3425.56 3,737 3653.87 4,059 3913.58 4,331 4250.79 5,144 4535.5

10 6,151 5386.911 7,316 6441.512 8,079 7661.513 8,602 8460.514 9,275 9008.215 9,791 9713.016 10,574 10253.417 11,354 11073.418 8,290 11890.219 9,750 8681.520 10,400 10210.521 11,177 10891.222 11,809 11704.823 -- 12366.7

Recreational places

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: F5By Changing: F3

Minimize: F5By Changing: F3

H5
Variable cell
H7
Set cell
Page 36: Ragsdale Fysco Food Decision Modeling Case

Series 4 - Growth

Page 36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Recreational places

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: F5By Changing: F3

Minimize: F5By Changing: F3

Page 37: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Holts

Page 37

Year Trend1 11,115 11115 0.00 -- alpha =2 11,357 11345 134.30 11115.0 beta =3 11,692 11681.45 252.34 11479.3

4 12,338 12318 476.65 11933.8 MSE =5 12,950 12942.29 562.89 12794.66 13,534 13532.57 578.88 13505.27 14,401 14386.64 739.56 14111.58 14,300 14341 281.07 15126.29 14,929 14913.78 451.41 14622.0

10 15,728 15710 652.75 15365.211 16,767 16747 877.08 16362.812 17,959 17942.39 1062.97 17624.013 18,983 18984.11 1050.56 19005.414 19,844 19853.46 944.75 20034.715 21,086 21071.73 1104.46 20798.216 22,093 22097.13 1058.30 22176.217 22,993 23001.06 968.16 23155.418 24,071 24066 1024.65 23969.219 25,141 25138.5 1052.62 25090.620 26,256 26252.78 1088.62 26191.121 27,016 27032.14 908.04 27341.422 28,012 28008.44 947.90 27940.223 -- -- -- 28956.3

Schools & colleges

Base Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 38: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Holts

Page 38

0.9504057490.584

87677.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

J5
Variable cell
J6
Variable cell
J8
Set cell
Page 39: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Regression

Page 39

Year1 11,115 9,447 intercept slope2 11,357 10,290 8604.4 843.03 11,692 11,1334 12,338 11,976 MSE =5 12,950 12,8196 13,534 13,6627 14,401 14,5058 14,300 15,3489 14,929 16,191

10 15,728 17,03411 16,767 17,87712 17,959 18,72013 18,983 19,56314 19,844 20,40615 21,086 21,24916 22,093 22,09217 22,993 22,93518 24,071 23,77819 25,141 24,62120 26,256 25,46421 27,016 26,30622 28,012 27,14923 -- 27,992

Schools & colleges

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 40: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Regression

Page 40

MSE Forecast614900.8 27992.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 41: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Growth

Page 41

Year1 11,115 -- g= 0.0446580592 11,357 11611.4

3 11,692 11864.2 MSE = 69928.34 12,338 12214.15 12,950 12889.06 13,534 13528.37 14,401 14138.48 14,300 15044.19 14,929 14938.6

10 15,728 15595.711 16,767 16430.412 17,959 17515.813 18,983 18761.014 19,844 19830.715 21,086 20730.216 22,093 22027.717 22,993 23079.618 24,071 24019.819 25,141 25146.020 26,256 26263.721 27,016 27428.522 28,012 28222.523 -- 29263.0

Schools & colleges

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

H5
Variable cell
H7
Set cell
Page 42: Ragsdale Fysco Food Decision Modeling Case

Series 5 - Growth

Page 42

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Schools & colleges

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 43: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Holts

Page 43

Year All other Trend1 16,194 16194 0.00 -- alpha =2 17,751 17751 780.03 16194.0 beta =3 18,663 18663 846.14 18531.0

4 19,077 19077 629.65 19509.1 MSE =5 20,047 20047 800.16 19706.66 20,133 20133 442.38 20847.27 20,755 20755 532.37 20575.48 21,122 21122 449.52 21287.49 22,887 22887 1108.55 21571.5

10 24,581 24581 1401.85 23995.611 26,198 26198 1509.64 25982.912 27,108 27108 1209.23 27707.613 27,946 27946 1023.25 28317.214 28,031 28031 553.20 28969.315 28,208 28208 364.73 28584.216 28,597 28597 376.89 28572.717 28,981 28981 380.45 28973.918 30,926 30926 1164.26 29361.519 31,926 31926 1081.97 32090.320 33,560 33560 1358.53 33008.021 34,508 34508 1152.86 34918.522 35,004 35004 823.79 35660.923 -- -- -- 35827.8

Base Level

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 44: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Holts

Page 44

10.501

492037.9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

Minimize: H6By Changing: H3:H4Subject To: 0<=H3:H4<=1

J5
Variable cell
J6
Variable cell
J8
Set cell
Page 45: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Regression

Page 45

Year All other1 16,194 16,335 intercept slope2 17,751 17,213 15457.0 878.13 18,663 18,0914 19,077 18,969 MSE =5 20,047 19,8476 20,133 20,7257 20,755 21,6038 21,122 22,4819 22,887 23,360

10 24,581 24,23811 26,198 25,11612 27,108 25,99413 27,946 26,87214 28,031 27,75015 28,208 28,62816 28,597 29,50617 28,981 30,38418 30,926 31,26219 31,926 32,14020 33,560 33,01821 34,508 33,89622 35,004 34,77423 -- 35,652

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 46: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Regression

Page 46

MSE Forecast520190.6 35652.3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res

Page 47: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Growth

Page 47

Year All other1 16,194 -- g= 0.0339363472 17,751 16743.6

3 18,663 18353.4 MSE = 382278.34 19,077 19296.45 20,047 19724.46 20,133 20727.37 20,755 20816.28 21,122 21459.39 22,887 21838.8

10 24,581 23663.711 26,198 25415.212 27,108 27087.113 27,946 28027.914 28,031 28894.415 28,208 28982.316 28,597 29165.317 28,981 29567.518 30,926 29964.519 31,926 31975.520 33,560 33009.521 34,508 34698.922 35,004 35679.123 -- 36191.9

Predicted Expenditures

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res

H5
Variable cell
H7
Set cell
Page 48: Ragsdale Fysco Food Decision Modeling Case

Series 6 - Growth

Page 48

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

All other

Predicted Expenditures

Year

Ex

pe

nd

itu

res