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Chapter-4.-Forecasting-HW.xlsx
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Q5

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Exponential smoothing
Alpha 0.3
Data
LABS07: Forecasting: Submodel = 13; Problem size @ 1 by 1		 Forecasts and Error Analysis
Period Demand Forecast Error Absolute Squared Abs Pct Err
Period 1 61 58 3 3 9 0.0491803279
Total 3 3 9 0.0491803279
Average 3 3 9 0.0491803279
Bias MAD MSE MAPE
SE ERROR:#NUM!
Next period 58.9 Not enough data to compute the standard error
Given an actual demand this period of​ 61, a forecast for this period of​ 58, and an alpha of​ 0.3, what would the forecast for the next period be using exponential​ smoothing?

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

Q8b

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Moving averages - 3 period moving average
Num pds 3 To change the number of periods use the scrollbar, do not change the cell itself
Data
LABS07: Forecasting: Submodel = 11; Problem size @ 11 by 3		 Forecasts and Error Analysis
Year Demand Forecast Error Absolute Squared Abs Pct Err
1 8
2 9
3 6
4 9 7.6666666667 1.3333333333 1.3333333333 1.7777777778 14.81%
5 11 8 3 3 9 27.27%
6 8 8.6666666667 -0.6666666667 0.6666666667 0.4444444444 08.33%
7 12 9.3333333333 2.6666666667 2.6666666667 7.1111111111 22.22%
8 13 10.3333333333 2.6666666667 2.6666666667 7.1111111111 20.51%
9 10 11 -1 1 1 10.00%
10 11 11.6666666667 -0.6666666667 0.6666666667 0.4444444444 06.06%
11 6 11.3333333333 -5.3333333333 5.3333333333 28.4444444444 88.89%
Total 2 17.3333333333 55.3333333333 198.11%
Average 0.25 2.1666666667 6.9166666667 24.76%
Bias MAD MSE MAPE
SE 3.036811193
Next period 9
The following table shows the actual demand observed over the last 11​ years:

Errors as a function of n

Enter the past demands in the data area

Q8c

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Weighted moving averages - 3 period moving average
Data
LABS07: Forecasting: Submodel = 12; Problem size @ 11 by 3		 Forecasts and Error Analysis
Year Demand Weights Forecast Error Absolute Squared Abs Pct Err
1 8 0.1 3 periods ago
2 9 0.3 2 periods ago
3 6 0.6 1 periods ago
4 9 7.1 1.9 1.9 3.61 21.11%
5 11 8.1 2.9 2.9 8.41 26.36%
6 8 9.9 -1.9 1.9 3.61 23.75%
7 12 9 3 3 9 25.00%
8 13 10.7 2.3 2.3 5.29 17.69%
9 10 12.2 -2.2 2.2 4.84 22.00%
10 11 11.1 -0.1 0.1 0.01 00.91%
11 6 10.9 -4.9 4.9 24.01 81.67%
Total 1 19.2 58.78 218.49%
Average 0.125 2.4 7.3475 27.31%
Bias MAD MSE MAPE
SE 3.1299627261
Next period 7.9
Using the​ 3-year weighted moving average with weights
0.150.15​,
0.300.30​,
and
0.550.55​,
using
0.550.55
for the most recent​ period, provide the forecast from periods 4 through 12​ (round your responses to two decimal​ places).

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

9a,b

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Moving averages - 2 period moving average
Num pds 2 To change the number of periods use the scrollbar, do not change the cell itself
Data
LABS07: Forecasting: Submodel = 11; Problem size @ 5 by 2		 Forecasts and Error Analysis
Year Mileage Forecast Error Absolute Squared Abs Pct Err
1 3100
2 3950
3 3400 3525 -125 125 15625 03.68%
4 3850 3675 175 175 30625 04.55%
5 3700 3625 75 75 5625 02.03%
Total 125 375 51875 10.25%
Average 41.6666666667 125 17291.6666666667 03.42%
Bias MAD MSE MAPE
SE 227.7608394786
Next period 3775
The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as​ follows:

Errors as a function of n

Enter the past demands in the data area

9c

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Weighted moving averages - 2 period moving average
Data
LABS07: Forecasting: Submodel = 12; Problem size @ 5 by 2		 Forecasts and Error Analysis
Year Mileage Weights Forecast Error Absolute Squared Abs Pct Err
1 3100 0.45 2 periods ago
2 3950 0.55 1 periods ago
3 3400 3567.5 -167.5 167.5 28056.25 04.93%
4 3850 3647.5 202.5 202.5 41006.25 05.26%
5 3700 3647.5 52.5 52.5 2756.25 01.42%
Total 87.5 422.5 71818.75 11.61%
Average 29.1666666667 140.8333333333 23939.5833333333 03.87%
Bias MAD MSE MAPE
SE 267.9902050449
Next period 3767.5
The forecast for year 6 using a weighted​ 2-year moving average with weights of
0.450.45
and
0.550.55
​(the weight of
0.550.55
is for the most recent​ period) =
37503750
miles ​(round your response to the nearest whole​ number).

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

9d

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Exponential smoothing
Alpha 0.4
Data
LABS07: Forecasting: Submodel = 13; Problem size @ 5 by 1		 Forecasts and Error Analysis
Year Mileage Forecast Error Absolute Squared Abs Pct Err
1 3100 3100 0 0 0 00.00%
2 3950 3100 850 850 722500 21.52%
3 3400 3440 -40 40 1600 01.18%
4 3850 3424 426 426 181476 11.06%
5 3700 3594.4 105.6 105.6 11151.36 0.0285405405
Total 1341.6 1421.6 916727.36 36.61%
Average 268.32 284.32 183345.472 07.32%
Bias MAD MSE MAPE
SE 552.7890978182
Next period 3636.64
Using exponential smoothing with
alphaα
​=
0.300.30
and the forecast for year 1 being
3 comma 0503,050​,
the forecast for year 6​ =
35713571
miles ​(round your response to the nearest whole​ number).

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

10b

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Moving averages - 3 period moving average
Num pds 3 To change the number of periods use the scrollbar, do not change the cell itself
Data
LABS07: Forecasting: Submodel = 11; Problem size @ 12 by 3		 Forecasts and Error Analysis
Month Sales Forecast Error Absolute Squared Abs Pct Err
Jan 21
Feb 20
Mar 15
Apr 12 18.6666666667 -6.6666666667 6.6666666667 44.4444444444 55.56%
May 13 15.6666666667 -2.6666666667 2.6666666667 7.1111111111 20.51%
Jun 16 13.3333333333 2.6666666667 2.6666666667 7.1111111111 16.67%
Jul 16 13.6666666667 2.3333333333 2.3333333333 5.4444444444 14.58%
Aug 17 15 2 2 4 11.76%
Sept 22 16.3333333333 5.6666666667 5.6666666667 32.1111111111 25.76%
Oct 22 18.3333333333 3.6666666667 3.6666666667 13.4444444444 16.67%
Nov 23 20.3333333333 2.6666666667 2.6666666667 7.1111111111 11.59%
Dec 23 22.3333333333 0.6666666667 0.6666666667 0.4444444444 02.90%
Total 10.3333333333 29 121.2222222222 176.00%
Average 1.1481481481 3.2222222222 13.4691358025 19.56%
Bias MAD MSE MAPE
SE 4.1614252748
Next period 22.6666666667
The monthly sales for Yazici​ Batteries, Inc., were as​ follows:

Errors as a function of n

Enter the past demands in the data area

10b cont

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Trend Projection
Data
LABS07: Forecasting: Submodel = 12; Problem size @ 12 by 6		 Forecasts and Error Analysis
Month Sales Weights Forecast Error Absolute Squared Abs Pct Err
Jan 21 0.1 6 periods ago
Feb 20 0.1 5 periods ago
Mar 15 0.1 4 periods ago
Apr 12 0.2 3 periods ago
May 13 0.2 2 periods ago
Jun 16 0.3 1 periods ago
Jul 16 15.4 0.6 0.6 0.36 03.75%
Aug 17 15.3 1.7 1.7 2.89 10.00%
Sept 22 15.5 6.5 6.5 42.25 29.55%
Oct 22 17.3 4.7 4.7 22.09 21.36%
Nov 23 18.9 4.1 4.1 16.81 17.83%
Dec 23 20.6 2.4 2.4 5.76 10.43%
Total 20 20 90.16 92.92%
Average 3.3333333333 3.3333333333 15.0266666667 15.49%
Bias MAD MSE MAPE
SE 4.7476309882
Next period 21.4
The forecast for the next period​ (Jan) using a​ 3-month moving average approach​ =
21.6721.67
sales ​(round your response to two decimal​ places).

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

10c

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Trend projection
Data
LABS07: Forecasting: Submodel = 14; Problem size @ 12 by 6		 Forecasts and Error Analysis
Month Sales Period(x) Forecast Error Absolute Squared Abs Pct Err
Jan 21 1 15.2564102564 5.7435897436 5.7435897436 32.9888231427 27.35%
Feb 20 2 15.8158508159 4.1841491841 4.1841491841 17.5071043952 20.92%
Mar 15 3 16.3752913753 -1.3752913753 1.3752913753 1.891426367 09.17%
Apr 12 4 16.9347319347 -4.9347319347 4.9347319347 24.3515792677 41.12%
May 13 5 17.4941724942 -4.4941724942 4.4941724942 20.1975864074 34.57%
Jun 16 6 18.0536130536 -2.0536130536 2.0536130536 4.217326574 12.84%
Jul 16 7 18.6130536131 -2.6130536131 2.6130536131 6.8280491847 16.33%
Aug 17 8 19.1724941725 -2.1724941725 2.1724941725 4.7197309295 12.78%
Sept 22 9 19.7319347319 2.2680652681 2.2680652681 5.1441200602 10.31%
Oct 22 10 20.2913752914 1.7086247086 1.7086247086 2.9193983949 07.77%
Nov 23 11 20.8508158508 2.1491841492 2.1491841492 4.6189925071 09.34%
Dec 23 12 21.4102564103 1.5897435897 1.5897435897 2.5272846811 06.91%
Total 0 35.2867132867 127.9114219114 209.41%
Intercept 14.696969697 Average 0 2.9405594406 10.6592851593 17.45%
Slope 0.5594405594 Bias MAD MSE MAPE
SE 3.5764706333
Future period 21.9696969697 13
Correlation 0.509117137
Coefficient of determination 0.2592002592
The method that can be used for making a forecast for the month of March is trend projection

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

11a


Computer Services: Created by Excel OM/QM version 5.2.116
Forecasting Weighted moving averages - 2 period moving average
Data
Computer Services: Forecasting: Submodel = 12; Problem size @ 12 by 2		 Forecasts and Error Analysis
Month Price Per Chip Weights Forecast Error Absolute Squared Abs Pct Err
January 1.9 1.88 2 periods ago
February 1.61 1.73 1 periods ago
March 1.6 1.7610249307 -0.1610249307 0.1610249307 0.0259290283 10.06%
April 1.85 1.6052077562 0.2447922438 0.2447922438 0.0599232426 13.23%
May 1.88 1.7198060942 0.1601939058 0.1601939058 0.0256620875 08.52%
June 1.89 1.8643767313 0.0256232687 0.0256232687 0.0006565519 01.36%
July 2 1.8847922438 0.1152077562 0.1152077562 0.0132728271 05.76%
August 1.75 1.9427146814 -0.1927146814 0.1927146814 0.0371389484 11.01%
September 1.7 1.8801939058 -0.1801939058 0.1801939058 0.0324698437 10.60%
October 1.6 1.7260387812 -0.1260387812 0.1260387812 0.0158857744 07.88%
November 1.5 A 1.6520775623 -0.1520775623 0.1520775623 0.023127585 10.14%
December 1.75 1.5520775623 0.1979224377 0.1979224377 0.0391732913 11.31%
Total -0.0683102493 1.5557894737 0.2732391802 89.87%
Average -0.0068310249 0.1555789474 0.027323918 08.99%
Bias MAD MSE MAPE
SE 0.1848104367
Next period 1.6198060942 C
Lenovo uses the​ ZX-81 chip in some of its laptop computers. The prices for the chip during the last 12 months were as​ follows:

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

11b


Computer Services: Created by Excel OM/QM version 5.2.116
Forecasting Weighted moving averages - 3 period moving average
Data
Computer Services: Forecasting: Submodel = 12; Problem size @ 12 by 3		 Forecasts and Error Analysis
Month Price Per Chip Weights Forecast Error Absolute Squared Abs Pct Err
January 1.9 1.92 3 periods ago
February 1.61 1.88 2 periods ago
March 1.6 1.82 1 periods ago
April 1.85 1.7058362989 0.1441637011 0.1441637011 0.0207831727 07.79%
May 1.88 1.6843772242 0.1956227758 0.1956227758 0.0382682704 10.41%
June 1.89 1.7743060498 0.1156939502 0.1156939502 0.0133850901 06.12%
July 2 1.8729893238 0.1270106762 0.1270106762 0.0161317119 06.35%
August 1.75 1.9222064057 -0.1722064057 0.1722064057 0.0296550462 09.84%
September 1.7 1.8814590747 -0.1814590747 0.1814590747 0.0329273958 10.67%
October 1.6 1.8192170819 -0.2192170819 0.2192170819 0.048056129 13.70%
November 1.5 B 1.6846975089 -0.1846975089 0.1846975089 0.0341131698 12.31%
December 1.75 1.6017793594 0.1482206406 0.1482206406 0.0219693583 08.47%
Total -0.0268683274 1.4882918149 0.2552893441 85.67%
Average -0.0029853697 0.1653657572 0.0283654827 09.52%
Bias MAD MSE MAPE
SE 0.1909709567
Next period 1.6151245552 C

Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.

12a

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Simple Linear Regression
Alpha 0.6
Data
LABS07: Forecasting: Submodel = 13; Problem size @ 5 by 1		 Forecasts and Error Analysis
Year Heart Transplants Forecast Error Absolute Squared Abs Pct Err
1 45 41 4 4 16 08.89%
2 50 43.4 6.6 6.6 43.56 13.20%
3 53 47.36 5.64 5.64 31.8096 10.64%
4 54 50.744 3.256 3.256 10.601536 06.03%
5 57 52.6976 4.3024 4.3024 18.51064576 0.0754807018
Total 23.7984 23.7984 120.48178176 46.31%
Average 4.75968 4.75968 24.096356352 09.26%
Bias MAD MSE MAPE
SE 6.3372386668
Next period 55.27904
Make sure to update the forecast with the value from the problem
D8
Following are two weekly forecasts made by two different methods for the number of gallons of​ gasoline, in​ thousands, demanded at a local gasoline station. Also shown are actual demand​ levels, in thousands of​ gallons:

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

13


Computer Services: Created by Excel OM/QM version 5.2.116
Forecasting Exponential smoothing
Alpha 0.21
Data
Computer Services: Forecasting: Submodel = 13; Problem size @ 4 by 1		 Forecasts and Error Analysis
Time Period t Actual Demand Forecast Error Absolute Squared Abs Pct Err
1 50 50 0 0 0 00.00%
2 48 50 -2 2 4 04.17%
3 56 49.58 6.42 6.42 41.2164 11.46%
4 45 50.9282 -5.9282 5.9282 35.14355524 0.1317377778
Total -1.5082 14.3482 80.35995524 28.80%
Average -0.37705 3.58705 20.08998881 07.20%
Bias MAD MSE MAPE
SE 6.3387678314
Next period 49.683278
Errors as a function of alpha
Alpha Error Absolute Squared Abs Pct Err Standard Err
0 -0.37705 3.58705 20.08998881 07.20% 6.3387678314
0.1 -0.305 3.405 17.9541 0.0682063492 5.9923451169
0.2 -0.37 3.57 19.8836 0.0716547619 6.3061240077
0.3 -0.445 3.745 22.0661 0.0753253968 6.6432070568
0.4 -0.53 3.93 24.5316 0.079218254 7.004512831
0.5 -0.625 4.125 27.3125 0.0833333333 7.3908727495
0.6 -0.73 4.33 30.4436 0.0876706349 7.8030250544
0.7 -0.845 4.545 33.9621 0.0922301587 8.2416139196
0.8 -0.97 4.77 37.9076 0.0970119048 8.7071924293
0.9 -1.105 5.005 42.3221 0.102015873 9.200228258
1 -1.25 5.25 47.25 0.1072420635 9.7211110476
Forecast for period 3 = Forecast for period 2 + (alpha * Error)
50.88=49.6+a*(56-49.6)

Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast.

14

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Simple Linear Regression
Data
LABS07: Forecasting: Submodel = 15; Problem size @ 10 by 1		 Forecasts and Error Analysis
Observation Number of Patients Year Forecast Error Absolute Squared Abs Pct Err
1 37 1 33.5272727273 3.4727272727 3.4727272727 12.0598347107 09.39%
2 33 2 36.8545454545 -3.8545454545 3.8545454545 14.8575206612 11.68%
3 39 3 40.1818181818 -1.1818181818 1.1818181818 1.3966942149 03.03%
4 42 4 43.5090909091 -1.5090909091 1.5090909091 2.2773553719 03.59%
5 42 5 46.8363636364 -4.8363636364 4.8363636364 23.3904132231 11.52%
6 56 6 50.1636363636 5.8363636364 5.8363636364 34.0631404959 10.42%
7 62 7 53.4909090909 8.5090909091 8.5090909091 72.4046280992 13.72%
8 54 8 56.8181818182 -2.8181818182 2.8181818182 7.9421487603 05.22%
9 58 9 60.1454545455 -2.1454545455 2.1454545455 4.6029752066 03.70%
10 62 10 63.4727272727 -1.4727272727 1.4727272727 2.1689256198 02.38%
Total 0 35.6363636364 175.1636363636 74.64%
Intercept 30.2 A Average 0 3.5636363636 17.5163636364 07.46%
Slope 3.3272727273 A Bias MAD MSE MAPE
SE 4.6792579054
Forecast 66.8 11 B
Correlation 0.9160119889
Replace the 11 with 12 C Coefficient of determination 0.8390779638
Dr. Lillian​ Fok, a New Orleans​ psychologist, specializes in treating patients who are agoraphobic​ (i.e., afraid to leave their​ homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same​ year:

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

15


Computer Services: Created by Excel OM/QM version 5.2.116		 Simple Linear Regression
Forecasting Simple linear regression
Data
Computer Services: Forecasting: Submodel = 15; Problem size @ 6 by 1		 Forecasts and Error Analysis
Period Demand Period(x) Forecast Error Absolute Squared Abs Pct Err
1 7 1 6.380952381 0.619047619 0.619047619 0.3832199546 08.84%
2 9 2 7.4952380952 1.5047619048 1.5047619048 2.26430839 16.72%
3 5 3 8.6095238095 -3.6095238095 3.6095238095 13.0286621315 72.19%
4 11 4 9.7238095238 1.2761904762 1.2761904762 1.6286621315 11.60%
5 10 5 10.8380952381 -0.8380952381 0.8380952381 0.7024036281 08.38%
6 13 6 11.9523809524 1.0476190476 1.0476190476 1.0975056689 08.06%
Total 0 8.8952380952 19.1047619048 125.79%
Intercept 5.2666666667 Average 7.40148683083438E-16 1.4825396825 3.1841269841 20.97%
Slope 1.1142857143 Bias MAD MSE MAPE
SE 2.1854497194
Forecast 13.0666666667 7
Correlation 0.7294712331
Coefficient of determination 0.5321282799
Given the following​ data, use least squares regression to derive a trend​ equation:
The least squares regression equation that shows the best relationship between demand and period is ​(round your responses to two decimal​ places):

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

16


Computer Services: Created by Excel OM/QM version 5.2.116		 Simple Linear Regression
Forecasting Simple linear regression
Data
Computer Services: Forecasting: Submodel = 15; Problem size @ 4 by 1		 Forecasts and Error Analysis
Number of Accidents Month Forecast Error Absolute Squared Abs Pct Err
Jan 30 1 26 4 4 16 13.33%
Feb 45 2 49.5 -4.5 4.5 20.25 10.00%
Mar 70 3 73 -3 3 9 04.29%
Apr 100 4 96.5 3.5 3.5 12.25 03.50%
Total 0 15 57.5 31.12%
Intercept 2.5 Average 0 3.75 14.375 07.78%
Slope 23.5 Bias MAD MSE MAPE
SE 5.3619026474
Forecast 120 5
Correlation 0.9897478906
Coefficient of determination 0.9796008869
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4​ months:

If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y.

19a

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Simple Linear Regression
Num pds 8 To change the number of periods use the scrollbar, do not change the cell itself
Data
LABS07: Forecasting: Submodel = 11; Problem size @ 12 by 1		 Forecasts and Error Analysis
Month Unit Sales Management's Forecast Forecast Error Absolute Squared Abs Pct Err
Jul 102 -
Aug 93 -
Sep 95 -
Oct 108 -
Nov 124 -
Dec 119 -
Jan 92 -
Feb 83 -
Mar 101 120 120 -19 19 361 18.81%
Apr 98 116 116 -18 18 324 18.37%
May 90 110 110 -20 20 400 22.22%
Jun 108 112 112 -4 4 16 03.70%
Total -61 61 1101 0.6310515405
Average -15.25 15.25 275.25 0.1577628851
Bias MAD MSE MAPE
SE 23.4627364133
Next period 101.875

Errors as a function of n

Enter the past demands in the data area

19b

Forecasting
LABS07: Created by Excel OM/QM version 5.2.101		 Moving averages - 1 period moving average
Num pds 1 To change the number of periods use the scrollbar, do not change the cell itself
Data
LABS07: Forecasting: Submodel = 11; Problem size @ 12 by 1		 Forecasts and Error Analysis
Month 2009-2010 Unit Sales Forecast Error Absolute Squared Abs Pct Err
Jul 102
Aug 93 102 -9 9 81 09.68%
Sep 95 93 2 2 4 02.11%
Oct 108 95 13 13 169 12.04%
Nov 124 108 16 16 256 12.90%
Dec 119 124 -5 5 25 04.20%
Jan 92 119 -27 27 729 29.35%
Feb 83 92 -9 9 81 10.84%
Mar 101 83 18 18 324 17.82%
Apr 98 101 -3 3 9 03.06%
May 90 98 -8 8 64 08.89%
Jun 108 90 18 18 324 16.67%
Total 6 128 2066 127.55%
Average 0.5454545455 11.6363636364 187.8181818182 11.60%
Bias MAD MSE MAPE
SE 15.1510909032
Next period 108

Errors as a function of n

Enter the past demands in the data area