Stationar Trend 1 Trend 2 Trend 3 Seasonal Seasonal SeasonalMultiplicative.Seasnl.Quad65 424 12690 400 7130 10,159 147.6 $684.2 61 418 13910 410 6940 11,175 251.8 $584.1 68 435 14040 427 7354 12,310 273.1 $765.4 67 449 14210 440 7556 12,446 249.1 $892.3 74 446 14740 444 7673 13,213 139.3 $885.4 82 469 14660 428 7332 16,412 221.2 $677.0 75 500 16310 449 7662 17,405 260.2 ###63 481 17110 456 7809 14,233 259.5 ###72 489 17680 444 7872 14,606 140.5 ###78 506 17450 450 7551 12,969 245.5 $993.2 73 530 18470 474 7989 13,980 298.8 ###75 532 18490 417 8143 14,755 287 ###68 546 19200 420 8167 12,300 135.04 ###64 557 18590 386 7902 13,224 258.08 ###73 559 18800 423 8268 13,606 314.8 ###79 554 20000 382 8436 13,659 323.44 ###

573 20290 387 16,442 207.76 ###574 19330 412 17,334 320.88 ###604 19250 396 19,605 371.68 ###611 20290 396 18,997 383.76 ###626 19440 383 15,971 264.4636 20070 386 15,740 402.6649 19800 395 16,919 411.3643 19700 390 18,931 385.9

392 279.24378 371.04330 372.84352 351.6319 246.12339 281.28

342.48310.44231.84316.44

351378.24213.96329.4

354.48343.68228.96316.2

382.56366.36291.12382.56395.52405.84278.52342.72349.2

337.68

Multiplicative.Seasnl.Quad

INPUTS OUTPUTSNumber of Periods in Moving Average = Forecast Number of Periods of Data Collected = MSE = MAPE =

MAD = LAD =

Absolute Error AbsolutePeriod Value Forecast Error Error Squared % Error

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INPUTS OUTPUTSNumber of Periods in Moving Average = Forecast Number of Periods of Data Collected = MSE = MAPE =

MAD = LAD =

Past Period 1 2 3 4 5 6 7 8Weighting

Absolute Error AbsolutePeriod Value Forecast Error Error Squared % Error

9 10 11 12 13 14 15

INPUTS OUTPUTSNumber of Periods of Data Collected = Forecast Smoothing Constant (alpha) = MSE = MAPE =Initial Forecast Value = MAD = LAD =

Absolute Error AbsolutePeriod Value Forecast Error Error Squared % Error

INPUTS OUTPUTSNumber of Periods of Data Collected 24 Period

MSE = 826877.58MAD = 816.361

OUTPUTSPeriod 25 26 27 28 29 30 31Forecast 21586.268 21898.1 22209.94 22521.77 22833.61 23145.44 23457.276812

Absolute Error Absoluteperiod sq Period Value Forecast Error Error Squared % Error

1 1 12690 14102.23 -1412.23 1412.233 1994403 0.1112874 2 13910 14414.07 -504.068 504.0681 254084.7 0.0362389 3 14040 14725.9 -685.903 685.9029 470462.8 0.04885316 4 14210 15037.74 -827.738 827.7377 685149.7 0.0582525 5 14740 15349.57 -609.572 609.5725 371578.6 0.04135536 6 14660 15661.41 -1001.41 1001.407 1002816 0.06830949 7 16310 15973.24 336.758 336.758 113405.9 0.02064764 8 17110 16285.08 824.9232 824.9232 680498.3 0.04821381 9 17680 16596.91 1083.088 1083.088 1173080 0.061261

100 10 17450 16908.75 541.2536 541.2536 292955.5 0.031017121 11 18470 17220.58 1249.419 1249.419 1561047 0.067646144 12 18490 17532.42 957.5841 957.5841 916967.2 0.051789169 13 19200 17844.25 1355.749 1355.749 1838056 0.070612196 14 18590 18156.09 433.9145 433.9145 188281.8 0.023341225 15 18800 18467.92 332.0797 332.0797 110276.9 0.017664256 16 20000 18779.76 1220.245 1220.245 1488998 0.061012289 17 20290 19091.59 1198.41 1198.41 1436187 0.059064324 18 19330 19403.42 -73.4246 73.42464 5391.177 0.003798361 19 19250 19715.26 -465.259 465.2594 216466.3 0.024169400 20 20290 20027.09 262.9058 262.9058 69119.46 0.012957441 21 19440 20338.93 -898.929 898.929 808073.3 0.046241484 22 20070 20650.76 -580.764 580.7638 337286.6 0.028937529 23 19800 20962.6 -1162.6 1162.599 1351635 0.058717576 24 19700 21274.43 -1574.43 1574.433 2478840 0.07992

Forecast

MAPE = 4.714LAD = 1574.433

32 33 34 35 3623769.11 24080.95 24392.78 24704.62 25016.45

SUMMARY OUTPUT

Regression StatisticsMultiple R SquareAdjusted Standard Observati

ANOVA

RegressioResidualTotal

CoefficientsInterceptperiod sqPeriod

RESIDUAL OUTPUT

Observation12345678

9101112131415161718192021222324

SUMMARY OUTPUT

Regression Statistics0.9816080.9635530.960082478.0411

24

df SS MS F Significance F2 1.27E+08 63436572 277.5935 7.9E-1621 4798989 228523.323 1.32E+08

CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%11783.75 318.9498 36.94545 1.36E-20 11120.45 12447.04 11120.45 12447.04-18.523 2.282779 -8.11421 6.56E-08 -23.2703 -13.7757 -23.2703 -13.7757774.9087 58.78471 13.18215 1.26E-11 652.6592 897.1582 652.6592 897.1582

RESIDUAL OUTPUT

Predicted ValueResiduals12540.13 149.869213259.47 650.529413941.76 98.2355414587.01 -377.01215195.21 -455.21515766.37 -1106.3716300.48 9.51909416797.55 312.4548

17257.56 422.436317680.54 -230.53618066.46 403.537218415.34 74.6565218727.18 472.821719001.97 -411.96719239.71 -439.7119440.41 559.592919604.06 685.941719730.66 -400.66419820.22 -570.22319872.74 417.263819888.2 -448.20419866.63 203.3747

19808 -8.00119712.33 -12.3308

INPUTS OUTPUTSNumber of Periods of Data Collected = Period Forecast Smoothing Constant (alpha) =Smoothing Constant (gamma) = MSE = MAPE =Initial Forecast Value (Level) = MAD = LAD =Initial Forecast Value (Trend) =

OUTPUTSPeriodForecast

Forecast Forecast Absolute Error AbsolutePeriod Value Level Trend Forecast Error Error Squared % Error

Enter the time series starting at B7. MSE269992.9

Periods in MAVG k1 6 Number of forecastsPeriods in DMAVG k2 4 2

Time Time Period Series MAVG DMAVG Et Tt Forecast Error

1 126902 139103 140404 142105 147406 14660 14041.677 16310 146458 17110 15178.339 17680 15785 14912.5 16657.5 581.666710 17450 16325 15483.33 17166.67 561.1111 17239.17 210.833311 18470 16946.67 16058.75 17834.58 591.9444 17727.78 742.222212 18490 17585 16660.42 18509.58 616.3889 18426.53 63.4722213 19200 18066.67 17230.83 18902.5 557.2222 19125.97 74.0277814 18590 18313.33 17727.92 18898.75 390.2778 19459.72 -869.72215 18800 18500 18116.25 18883.75 255.8333 19289.03 -489.02816 20000 18925 18451.25 19398.75 315.8333 19139.58 860.416717 20290 19228.33 18741.67 19715 324.4444 19714.58 575.416718 19330 19368.33 19005.42 19731.25 241.9444 20039.44 -709.44419 19250 19376.67 19224.58 19528.75 101.3889 19973.19 -723.19420 20290 19660 19408.33 19911.67 167.7778 19630.14 659.861121 19440 19766.67 19542.92 19990.42 149.1667 20079.44 -639.44422 20070 19778.33 19645.42 19911.25 88.61111 20139.58 -69.583323 19800 19696.67 19725.42 19667.92 -19.1667 19999.86 -199.86124 19700 19758.33 19750 19766.67 5.555556 19648.75 51.2525 19772.2226 19777.78

Enter K1>2 and K2>2 in E3 and E4. Don't change column A.

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SeriesLinear (Series)

Do not interfer with the hiddend columns K and L

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SeriesLinear (Series)

INPUTSNumber of Periods of Data Collected =Number of Seasons =

Seasonal FactorsValue Period 1 2 3 4 5 6

7 8 9 10 11