Download Breakthroughs in statistics. - Methodology and distribution by Samuel Kotz, Norman L. Johnson PDF

By Samuel Kotz, Norman L. Johnson

This is often the second one of a quantity choice of seminal papers within the statistical sciences written up to now a hundred years. those papers have each one had a good impact at the improvement of statistical conception and perform over the past century. each one paper is preceded by way of an advent written via an expert within the box supplying heritage info and assessing its impression. Readers will take pleasure in a clean outlook on now well-established positive factors of statistical concepts and philosophy by way of changing into accustomed to the methods they've been built. it really is was hoping that a few readers may be influenced to review the various references supplied within the creation (and additionally within the papers themselves) and so reach a deeper historical past wisdom of the foundation in their paintings.

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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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