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X2 X) 250,000 1,000,000 1,690,000 1,440,000 1,960,000 2,250,000 4,410,000 4,410,000 2,250,000 1,000,000 1,000,000 490,000 22,150,000
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when we rst characterized row d, it was with a focus on the large 2.16 value, which is the largest (negative) value in the table. This graphic, however, suggests more needs to be considered. Reexamining the data for that row we see that not only does that row have the largest negative value, but also two of the largest positive values. All together, we end up with a strong positive row effect. For individual cells, however, the effect may be low or high, depending on the column. Because the grand, row, and column effects represent the model, an assessment of that model requires an examination of where the model ts and does not t. The residuals for these models are presented in the center of Table 2.2. Examination of these values reveals that the extreme value observed in the raw data remains extreme in this model, and that this value is not simply the result of combining row and column effects. A graphical analog to the residuals can also be provided, as shown in Figure 2.12, panel B. In this diagram, lines are drawn from the value of the predicted values (the intersection of row and column lines), downward or upward to the actual data value. The length of each line thereby indicates the size of the residual. Clearly, the size of the trait residual for recognition tasks dwarfs the size of all other effects and residuals in the data. Other patterns of residuals may provide additional information about the data because they tell us what departs from a standard description. In this example we used simple raw residuals. In other applications, the actual value of residuals may be modi ed in a number of ways. One common method is to report residuals reexpressed as normal-deviates in the distribution of residuals. This approach, often used in structural equation analysis, can help identify the locations of the extremes, but hides the scale values of the error. In the highly developed area of regression diagnostics, residuals may be adjusted for the size of the leverage associated with the value of the criterion variable (studentized residuals) or calculated using a model that obtained predicted values without the presence of the observation in the model (externally studentized). This prevents extreme values from distorting the model to the point that an aberrant value leads to a small residual, as displayed in panel C of Figure 2.2. As illustrated previously, complementary to the notion of patterns of residuals and meaning in individual residuals is the emphasis on mathematical models of effects that provide rich and parsimonious description. This view is very much in line with the recently emerging view of the importance of effect sizes suggested by Glass (1976) and renewed by Cohen (1994) and the APA Task Force on Statistical Inference (Wilkinson, 1999). EDA reminds us that at the same time we focus on effects as a description of the data, we must also focus on the size and pattern of mis ts between effects and the data.
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ou re the man! You ve learned how to use Gmail to its fullest, and now you re writing scripts that use scraped APIs to control your mail. In the rest of the book, you take your skills to the next level. First, in 11, you look at organizing your mail using Gmail s labeling system. Then 12 deals with e-mail addresses and the import and export of addresses to the Gmail address book. Then, for a bit of a break, in 13 you look at the possibilities that might open up with the HTML-only version of Gmail. In the future, you might want to know about that so you can build your own API library. After that, it s back to practicalities, when you learn how to export mail in 14, use Gmail for all sorts of interesting activities ( 14), and then, in perhaps the culmination of the whole study of this fine web application, use Gmail as a mountable file system. Really. Peep 16 if you don t believe.
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= E V 2 (X1 , X2 , . . . , Xn )
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In a similar way in which RFID is intended to develop. In this context, environment means the telecommunication environment.
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