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Identified from its final variations because the "Bible of Forecasting", the 3rd variation of this authoritative textual content has followed a brand new approach-one that's as new because the most recent traits within the box: "Explaining the earlier isn't really enough for predicting the future". In different phrases, exact forecasting calls for greater than simply the ideal of versions to old info. inside of, readers will locate the most recent options utilized by managers in company this day, notice the significance of forecasting and learn the way it is complete. And readers will enhance the required abilities to fulfill the elevated call for for considerate and practical forecasts.

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2% indicate a good or bad forecasting performance? One basis for making such a comparison is to define some very simple na¨ıve methods against which the performance of more sophisticated methods can be compared. We have found it useful to define two different na¨ıve methods of forecasting for use as a basis in evaluating other methods in a given situation. The first is referred to as Na¨ıve Forecast 1 or NF1. This method uses the most recent observation available as a forecast. Table 2-11 shows NF1 used to forecast the monthly beer production.

This procedure is known as simple linear regression and will be examined in detail in Chapter 5. The mechanics of how a and b are calculated are not important at this point. 735 respectively. 735 × mileage. This line is shown in Figure 2-9. 27) 2/6 Least squares estimates 61 20 15 10 Price (thousands of $US) 25 Price/mileage relationship for Japanese automobiles 20 25 30 35 Mileage (mpg) Figure 2-9: Straight line estimate of vehicle price using mileage as an explanatory variable. It is important not to confuse an explanatory relationship (such as that between price and mileage) with a causal relationship.

Figure 2-11 displays the electricity data transformed using some of the transformations given in Table 2-17, showing the effect of the increasing strength of the transformations. Each of the transformations in Table 2-17 is a member of the family power transformation of power transformations: Wt =  p   −Yt , log(Yt ),   Y p, t p < 0; p = 0; p > 0. 28) For p = 1 the transformation is simply Wt = Yt , so this leaves the data alone. 0008 1960 1970 1980 1990 Figure 2-11: Transformations of the electricity production data.

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