Weighted moving averages 3 period moving average Data Forecasts and Error

Weighted moving averages 3 period moving average data

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2. Weighted moving averages - 3 period moving average Data Forecasts and Error Analysis Period Demand Weights Forecast Error Absolute January 10 1 3 periods ago February 12 2 2 periods ago March 13 3 1 periods ago April 16 12.1666667 3.83333333 3.83333333 May 19 14.3333333 4.66666667 4.66666667 June 23 17 6 6 July 26 20.5 5.5 5.5 August 30 23.8333333 6.16666667 6.16666667 September 28 27.5 0.5 0.5 October 18 28.3333333 -10.333333 10.3333333 November 16 23.3333333 -7.3333333 7.33333333 December 14 18.6666667 -4.6666667 4.66666667 Total 4.33333333 49 Average 0.48148148 5.44444444 Bias MAD SE January 15.33333 Enter the data in the shaded area. Enter weights in INCREASING order from top to bottom.
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Squared Abs Pct Err 14.6944444 23.96% 21.7777778 24.56% 36 26.09% 30.25 21.15% 38.0277778 20.56% 0.25 01.79% 106.777778 57.41% 53.7777778 45.83% 21.7777778 33.33% 323.333333 254.68% 35.9259259 28.30% MSE MAPE 6.79635757
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3. Exponential smoothing Alpha 0.2 Data Forecasts and Error Analysis Period Demand Forecast Error Absolute Squared Abs Pct Err February 153 142 11 11 121 0.0718954 Total 11 11 121 0.0718954 Average 11 11 121 0.0718954 Bias MAD MSE MAPE SE Err:502 Next period 144.2 Not enough data to com 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.
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mpute the standard error
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Trend Projection Alpha 0.2 Beta 0.4 Data Forecasts and Error Analysis Period Demand Error Absolute Month 1 12 11 2 13 -1 1 Month 2 17 12.8 1.92 14.72 2.28 2.28 Month 3 20 15.176 2.1024 17.2784 2.7216 2.7216 Month 4 19 17.82272 2.320128 20.142848 -1.142848 1.14285 Month 5 24 19.9142784 2.22870016 22.1429786 1.85702144 1.85702 Month 6 21 22.5143828 2.37726188 24.8916447 -3.8916447 3.89164 Month 7 31 24.1133158 2.0659303 26.1792461 4.82075392 4.82075 Month 8 28 27.1433969 2.45159061 29.5949875 -1.5949875 1.59499 Month 9 36 29.27599 2.32399161 31.5999816 4.40001841 4.40002 Next period 32.4799853 2.67599309 35.1559784 Total 8.44991358 23.7089 Average 0.93887929 2.63432 Bias MAD SE Smoothed Forecast, F t Smoothed Trend, T t Forecast Including Trend, FIT t Enter alpha and beta (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.
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Squared Abs Pct Err 1 08.33% 5.1984 13.41% 7.40710656 13.61% 1.30610155 06.01% 3.44852863 07.74% 15.1448987 18.53% 23.2396684 15.55% 2.54398504 05.70% 19.360162 0.12222273 78.6488508 101.11% 8.7387612 11.23% MSE MAPE 3.35194721
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5. Trend projection Data Forecasts and Error Analysis Period Demand (y) Period(x) Forecast Error Absolute Squared Period 1 74 1 67.25 6.75 6.75 45.5625 Period 2 79 2 77.7857143 1.21428571 1.21428571 1.4744898 Period 3 80 3 88.3214286 -8.3214286 8.32142857 69.2461735 Period 4 90 4 98.8571429 -8.8571429 8.85714286 78.4489796 Period 5 105 5 109.392857 -4.3928571 4.39285714 19.2971939 Period 6 142 6 119.928571 22.0714286 22.0714286 487.147959 Period 7 122 7 130.464286 -8.4642857 8.46428571 71.6441327 Total -4.26E-014 60.0714286 772.821429 Intercept 56.7142857 Average -6.09E-015 8.58163265 110.403061 Slope 10.5357143 Bias MAD MSE SE 12.4323886 Future period 141 8 Correlation 0.89490961 Coefficient of determination 0.80086321 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.
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Abs Pct Err 09.12% 01.54% 10.40% 09.84% 04.18% 15.54% 06.94% 57.57% 08.22% MAPE
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Simple Linear Regression 6. Multiplicative seasonal model 12 seasons Data Period Demand (y) Time (x) Average Ratio Month 1 80 1 94 0.85106383 Month 2 70 2 94 0.74468085 Month 3 80 3 94 0.85106383 Month 4 90 4 94 0.95744681 Month 5 113 5 94 1.20212766 Month 6 110 6 94 1.17021277 Month 7 100 7 94 1.06382979 Month 8 88 8 94 0.93617021 Month 9 85 9 94 0.90425532 Month 10 77 10 94 0.81914894 Month 11 75 11 94 0.79787234 Month 12 82 12 94 0.87234043 Month 13 85 13 94 0.90425532 Month 14 85 14 94 0.90425532 Month 15 93 15 94 0.9893617 Month 16 95 16 94 1.0106383 Month 17 125 17 94 1.32978723 Month 18 115 18 94 1.22340426 Month 19 102 19 94 1.08510638 Month 20 102 20 94 1.08510638 Month 21 90 21 94 0.95744681 Month 22 78 22 94 0.82978723 Month 23 82 23 94 0.87234043 Month 24 78 24 94 0.82978723 Month 25 105 25 94 1.11702128 Month 26 85 26 94 0.90425532 Month 27 82 27 94 0.87234043 Month 28 115 28 94 1.22340426 Month 29 131 29 94 1.39361702 Month 30 120 30 94 1.27659574 Month 31 113 31 94 1.20212766 Month 32
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