Data_Problem_Set_A

Data_Problem_Set_A - a) Moving Average Month 1 2 3 4 5 6 7...

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a) Moving Average Forecast Month Sales 1 42 2 50 3 51 4 58 5 50 50.25 0.25 0.25 0.06 6 62 52.25 -9.75 9.75 95.06 7 53 55.25 2.25 2.25 5.06 8 75 55.75 -19.25 19.25 370.56 9 76 60.00 -16.00 16.00 256.00 10 82 66.50 -15.50 15.50 240.25 11 98 71.50 -26.50 26.50 702.25 12 102 82.75 -19.25 19.25 370.56 13 89.50 MAD = 13.59 MSE= 254.98 Forecast Month Weight Month Sales 0.4 1 42 2 0.3 2 50 3 0.2 3 51 4 0.1 4 58 5 50 52.7 2.70 2.70 7.29 6 62 52.6 -9.40 9.40 88.36 7 53 56.5 3.50 3.50 12.25 8 75 55.6 -19.40 19.40 376.36 9 76 63.3 -12.70 12.70 161.29 10 82 69.7 -12.30 12.30 151.29 11 98 75.9 -22.10 22.10 488.41 12 102 86.5 -15.50 15.50 240.25 13 94.2 MAD= 12.20 MSE= 190.69 Forecast Error Absolute Forecast Error Squared Error I would recommend using the weighted moving average because it had a smaller MSE than the moving average technique. The technique with the smallest MSE is best at explaining the variability in the observations because when you square the error, you accentuate the variability that occurred. b) Weighted Moving Average Forecast Error Absolute Forecast Error Squared Error most recent
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Month Sales 1 42 42.00 42.00 42.00 2 50 42.00 42.00 42.00 8.00 8.00 8.00 16.00% 16.00% 16.00% 3 51 43.60 42.00 42.00 7.40 9.00 9.00 14.51% 17.65%
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This note was uploaded on 03/19/2008 for the course BUSI 403 taught by Professor Lu during the Spring '08 term at UNC.

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Data_Problem_Set_A - a) Moving Average Month 1 2 3 4 5 6 7...

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