Simple Regression Analysis produces the plot shown in Figure 6-7. This plot illustrates some of the problems you may encounter with historical data. (These types of data behavior problems are discussed in Section 6.2.) Although the historical observations from September 1997 through April 1998 are reasonably well behaved, the six monthly values recorded from May 1998 through August 1998 illustrate a great deal of variability. If the program had executed with a historical data series ending in May or June 1998, the resulting trend would have been negative. Therefore, you should be hesitant to base an extended forecast on this data series. Investigation of the historical data might reveal some reasons for the variations in their values. For example, a week of data might have been lost in May or a new workload might have been added to the system starting in June. Univariate Model Forecasting, which is discussed in Chapter 7, allows you to apply the results of this type of investigation by deleting or adjusting historical data points.
EXTENDED EXAMPLE CHANNEL 1 SIO COUNT VS MONTH SYSID: TSO1 ZONE: 1 PLOT OF CHAN1CNT*DATE LEGEND: A = 1 OBS, B = 2 OBS, ETC. PLOT OF PX*DATE SYMBOL USED IS * PLOT OF EST*DATE SYMBOL USED IS + 15000000 + A A 14000000 + + C 13000000 + + H + A + N + + 1 12000000 + * * S * I A A * * O * 11000000 + A C * * O * A A U * N A * T 10000000 +* A A A 9000000 + A 8000000 + -+----------+----------+----------+----------+----------+----------+----------+----------+----------+ 30SEP97 30NOV97 31JAN98 31MAR98 31MAY98 31JUL98 30SEP98 30NOV98 31JAN99 31MAR99 MONTH ENDING
Figure 6-7. Extended Example Simple Regression Forecast Plot
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