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By D. H. Stamatis

This article explains the instruments of records and the way to use them successfully to enhance methods and profitability in a company, and in addition delineates the significance of gathering, reading, and examining facts.

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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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