Lecture 4 - Forecasting - ABC_Corp

Lecture 4 - Forecasting - ABC_Corp - SUMMARY OUTPUT...

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SUMMARY OUTPUT Regression Statistics Multiple R 0.97 R Square 0.95 Adjusted R 0.95 Standard E 226.9 Observation 48 ANOVA df SS MS F ignificance F Regression 1 ### ### 882.92 0 Residual 46 ### 51484.28 Total 47 ### Coefficientstandard Erro t Stat P-value Lower 95%Upper 95%Lower 95.0% Upper 95.0% Intercept 5997.26 79.19 75.73 0 5837.85 6156.67 5837.85 6156.67 X Variable 70.25 2.36 29.71 0 65.49 75 65.49 75
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Static Method Sales 1997 1998 1999 2000 2001 JAN 2000 3000 2000 5000 5000 FEB 3000 4000 5000 4000 2000 MAR 3000 3000 5000 4000 3000 APR 3000 5000 3000 2000 2000 MAY 4000 5000 4000 5000 7000 JUN 6000 8000 6000 7000 6000 JUL 7000 3000 7000 10000 8000 AUG 6000 8000 10000 14000 10000 SEP 10000 12000 15000 16000 20000 OCT 12000 12000 15000 16000 20000 NOV 14000 16000 18000 20000 22000 DEC 8000 10000 8000 12000 8000 Total 78000 89000 98000 115000 113000 p = 12 (even) Year Month Period Forecast 1997 JAN 1 2000 6068 0.33 2588 588 1997 FEB 2 3000 6138 0.49 2913 -87 1997 MAR 3 3000 6208 0.48 2872 -128 1997 APR 4 3000 6278 0.48 2500 -500 1997 MAY 5 4000 6348 0.63 3944 -56 1997 JUN 6 6000 6419 0.93 5356 -644 1997 JUL 7 7000 6542 6489 1.08 5534 -1466 1997 AUG 8 6000 6625 6559 0.91 7551 1551 1997 SEP 9 10000 6667 6629 1.51 11489 1489 1997 OCT 10 12000 6750 6700 1.79 11913 -87 1997 NOV 11 14000 6875 6770 2.07 14377 377 1997 DEC 12 8000 7000 6840 1.17 7488 -512 1998 JAN 13 3000 6917 6910 0.43 2948 -52 1998 FEB 14 4000 6833 6981 0.57 3313 -687 1998 MAR 15 3000 7000 7051 0.43 3262 262 1998 APR 16 5000 7083 7121 0.70 2836 -2164 1998 MAY 17 5000 7167 7191 0.70 4468 -532 1998 JUN 18 8000 7333 7262 1.10 6059 -1941 1998 JUL 19 3000 7375 7332 0.41 6253 3253 1998 AUG 20 8000 7375 7402 1.08 8521 521 1998 SEP 21 12000 7500 7472 1.61 12950 950 1998 OCT 22 12000 7500 7543 1.59 13411 1411 1998 NOV 23 16000 7375 7613 2.10 16167 167 1998 DEC 24 10000 7250 7683 1.30 8411 -1589 1999 JAN 25 2000 7333 7753 0.26 3308 1308 1999 FEB 26 5000 7583 7824 0.64 3713 -1287 Demand D t Deseaso nalized Demand D t D t (based on regressio n) Seasonal Factor S t E t 1. Start by reformatting data Calculate Errors G Red Color codi
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1999 MAR 27 5000 7792 7894 0.63 3652 -1348 1999 APR 28 3000 8042 7964 0.38 3171 171 1999 MAY 29 4000 8250 8034 0.50 4992 992 1999 JUN 30 6000 8250 8105 0.74 6762 762 1999 JUL 31 7000 8292 8175 0.86 6972 -28 1999 AUG 32 10000 8375 8245 1.21 9491 -509 1999 SEP 33 15000 8292 8315 1.80 14411 -589 1999 OCT 34 15000 8208 8386 1.79 14910 -90 1999 NOV 35 18000 8208 8456 2.13 17958 -42 1999 DEC 36 8000 8292 8526 0.94 9334 1334 2000 JAN 37 5000 8458 8596 0.58 3667 -1333 2000 FEB 38 4000 8750 8667 0.46 4113 113 2000 MAR 39 4000 8958 8737 0.46 4042 42 2000 APR 40 2000 9042 8807 0.23 3507 1507 2000 MAY 41 5000 9167 8877 0.56 5516 516 2000 JUN 42 7000 9417 8948 0.78 7466 466 2000 JUL 43 10000 9583 9018 1.11 7691 -2309 2000 AUG 44 14000 9500 9088 1.54 10462 -3538 2000 SEP 45 16000 9375 9158 1.75 15871 -129 2000 OCT 46 16000 9333 9229 1.73 16409 409 2000 NOV 47 20000 9417 9299 2.15 19748 -252 2000 DEC 48 12000 9458 9369
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This note was uploaded on 07/04/2011 for the course MGSC 491 taught by Professor Nair during the Spring '11 term at South Carolina.

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Lecture 4 - Forecasting - ABC_Corp - SUMMARY OUTPUT...

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