
Finally, note that regression analysis forecasting relies on the assumption
that the past relationship between Xand Yremains the same during the forecast
period.
LIMITATIONS OF FORECASTING
Limitations of forecasting techniques, such as those illustrated in this section,
include:
Their use provides precise mathematical results that are only as good as
the data used. For example, if the guest night forecasts are not very good,
then the forecast for restaurant meals served will not be very good.
The mathematical approaches used in forecasting do not consider vari-
ables that can be controlled by management. For example, a forecast of
restaurant volume based on historic sales would need to be adjusted for
an anticipated increase in demand resulting from an increased advertis-
ing campaign that the restaurant manager is planning to implement.
No mathematical forecasting technique can substitute for experience and
individual judgment. Indeed, in some cases (such as opening a new prop-
erty or expanding an existing one) there may be only limited data avail-
able on which to base mathematical forecasting techniques, such as
moving averages or regression analysis. In such cases, judgment and other
qualitative considerations have to play a greater role.
FORECASTING 395
Guest Nights Meals Served XY X2
Month X Y (X ⴛY) (XⴛX)
1 6,102 7,822 47,729,844 37,234,404
2 6,309 7,544 47,595,096 39,803,481
3 6,384 8,021 51,206,064 40,755,456
4 6,501 8,299 53,951,799 42,263,001
5 6,498 8,344 54,219,312 42,224,004
6 6,382 8,245 52,619,590 40,729,924
7 6,450 8,311 53,605,950 41,602,500
8 6,522 8,274 53,963,028 42,536,484
9 6,608 8,328 55,031,424 43,665,664
10 6,502 8,188 53,238,376 42,276,004
11 6,274 7,985 50,097,890 39,363,076
12
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EXHIBIT 9.6
Calculation of Regression Analysis Data
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