Ch15_Forecasting

Ch15_Forecasting - Example 15.3 - Least Squares Regression...

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Example 15.3 - Least Squares Regression Analysis (1) (2) (3) (4) (5) (6) x y xy Ŷ 1 600 600 1 360,000 801.3 2 1,550 3,100 4 2,402,500 1,160.9 3 1,500 4,500 9 2,250,000 1,520.5 4 1,500 6,000 16 2,250,000 1,880.1 5 2,400 12,000 25 5,760,000 2,239.7 6 3,100 18,600 36 9,610,000 2,599.4 7 2,600 18,200 49 6,760,000 2,959.0 8 2,900 23,200 64 8,410,000 3,318.6 9 3,800 34,200 81 14,440,000 3,678.2 10 4,500 45,000 100 20,250,000 4,037.8 11 4,000 44,000 121 16,000,000 4,397.4 12 4,900 58,800 144 24,010,000 4,757.1 78 33,350 268,200 650 112,502,500 6.50 = X-bar 359.62 = b 2779.17 = Y-bar 441.67 = a Regression Equation is Y = 441.67 + 359.6*x x 2 y 2 0 2 4 6 8 10 12 1 0 1,000 2,000 3,000 4,000 5,000 6,000 Exhibit 15.4 Least Squares Regression Line Quarters Sales
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Exhibit 15.6 - Excel Regression Tool Quarter Sales 1 600 2 1,550 3 1,500 4 1,500 5 2,400 6 3,100 7 2,600 8 2,900 9 3,800 10 4,500 11 4,000 12 4,900 SUMMARY OUTPUT Regression Statistics Multiple R 0.97 R Square 0.93 Adjusted R Square 0.93 Standard Error 363.88 Observations 12 ANOVA df SS MS F Significance F Regression 1 ### ### 139.67 0 Residual 10 ### 132407.05 Total 11 ### Coefficientstandard Erro t Stat P-value Lower 95% Upper 95%Lower 95.0% Intercept 441.67 223.95 1.97 0.08 -57.33 940.66 -57.33 X Variable 1 359.62 30.43 11.82 0 291.82 427.42 291.82
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Upper 95.0% 940.66 427.42
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Exhibit 15.7 - Additive and Multiplicative Seasonal Variation 1.4 0.95 1 -70 0.93 0.9 2 -140 0.86 0.8 3 -280 0.72 0.6 4 -420 0.44 0.8 5 -280 0.72 0.9 6 -140 0.86 1.1 7 140 1.14 1.2 8 280 1.28 1.4 9 420 1.56 1.2 10 280 1.28 1.1 11 140 1.14 1.05 12 70 1.07 Month Trendline 1 1000 -70 930 0.93 930 2 1050 -140 910 0.86 903 3 1100 -280 820 0.72 792 4 1150 -420 730 0.44 506 5 1200 -280 920 0.72 864 6 1250 -140 1110 0.86 1075 7 1300 140 1440 1.14 1482 8 1350 280 1630 1.28 1728 9 1400 420 1820 1.56 2184 10 1450 280 1730 1.28 1856 11 1500 140 1640 1.14 1710 12 1550 70 1620 1.07 1658.5 13 1600 -70 1530 0.93 1488 14 1650 -140 1510 0.86 1419 15 1700 -280 1420 0.72 1224 16 1750 -420 1330 0.44 770 17 1800 -280 1520 0.72 1296 18 1850 -140 1710 0.86 1591 19 1900 140 2040 1.14 2166 20 1950 280 2230 1.28 2496 21 2000 420 2420 1.56 3120 22 2050 280 2330 1.28 2624 23 2100 140 2240 1.14 2394 24 2150 70 2220 1.07 2300.5 25 2200 -70 2130 0.93 2046 26 2250 -140 2110 0.86 1935 27 2300 -280 2020 0.72 1656 28 2350 -420 1930 0.44 1034 29 2400 -280 2120 0.72 1728 30 2450 -140 2310 0.86 2107 31 2500 140 2640 1.14 2850 32 2550 280 2830 1.28 3264 33 2600 420 3020 1.56 4056 34 2650 280 2930 1.28 3392 35 2700 140 2840 1.14 3078 36 2750 70 2820 1.07 2942.5 37 2800 -70 2730 0.93 2604 38 2850 -140 2710 0.86 2451 39 2900 -280 2620 0.72 2088 40 2950 -420 2530 0.44 1298 41 3000
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This note was uploaded on 07/17/2011 for the course MG 375 taught by Professor Fry during the Fall '09 term at Park.

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Ch15_Forecasting - Example 15.3 - Least Squares Regression...

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