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Abootalebi et al. (2006)1 Allen & Iacono (1997)2 Allen et al. (1992) Boaz et al. (1991)3 Ellwanger et al. (1996)4 Farwell & Donchin (1991)5 Johnson & Rosenfeld (1992) Rosenfeld, Angell et al. (1991)6 Rosenfeld, Biroschak et al. (2006)7 Rosenfeld, Cantwell et al. (1988) Rosenfeld, Ellwanger et al. (1999) Rosenfeld, Ellwanger et al. (1995)8 Rosenfeld, Shue et al. (2007)9 Rosenfeld, Soskins et al. (2004)10 Total 68 87 90 76 73 92 76 92 90 100 87 88 51 82 82.29 8 9% 8.75 91 87.50 32 0 10 24 27 0 24 8 10 0 13 12 49 18 16.21 16 0 4 24 0 0 21 13 18 0 0 84 72 96 76 100 83 79 87 82 100 100

12/03 Fundamental Characteristics Average Quality Yield to Maturity Modi ed Duration Average Maturity % Maturity < 3 years % Maturity 3 5 years % Maturity 5 10 years % Maturity > 10 years 12/02 12/01 12/00 12/99

Table 6.2 Statistics summary of data collected when S/A was switched on Map-matched DGPS Cross-track error Mean Standard Deviation 1.0 m 8.733 m Along-track error 2.1 m 17.548 m Position error 12.7 m 18.9 m Position error [95%] 9.3 m 11.3 m RAW Position error 35.727 m 42.842 m

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Current mobile users much more than 1 billion in 2004 are estimated to double by the year 2008, when, for example, Nokia forecasts that the value of the global mobile services business will be more than 600 billion euros. As indicated in Figure 2.1, voice revenues are a strongly dominating revenue source in the estimates but mobile data services will soon account for nearly one-third of the total pie. This will also be an area of more rapid growth, contrasted with the maturing voice revenue market. The share of data revenues is an important and keenly followed indicator, because the industry is worried about potential revenue decline and companies

ist is defined as:

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the responses to the six items have been divided by their sample standard deviations. Since the SEM used probit regressions for the binary responses, the parameter estimates for the logistic regression modes are standardized by dividing them by the standard deviation p of the standard logistic distribution: p/ 3 (see Agresti, 2002, p. 125 for more details). The conclusions from the significant parameter estimates for the SEM are very similar to those from the linear and logistic regression models. As noted when discussing Figure 22.2, the main difference is the lack of a significant selection effect of attitude in 1995 on beginning family care between 1995 and 1997. When the magnitudes of the significant standardized parameter estimates are compared for GCM and SEM, they are surprisingly similar given the difference in the approaches. The largest discrepancies are for the effects of beginning family care on family role attitude, where the estimates from the SEM are smaller in magnitude. To the extent that these differences are systematic, this could be a result of controlling for correlated measurement error in the responses to the six items used to measure the attitude.

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When it comes to the question of whether all white food is bad, we ve got to say that this isn t, ahem, a black and white issue. Indeed, as we note a number of times in this book, no food is forbidden just because you have diabetes, and that includes any and all white foods even white marshmallows! Of course, just because a food isn t forbidden doesn t automatically make that food a nutritious choice that should be consumed routinely or in unlimited quantities. A main reason why white foods sometime have a bad reputation is because certain white foods have a high glycemic index, meaning that they have a greater tendency to raise blood glucose than do some other foods of equal carbohydrate content. (We discuss the glycemic index in 4.) There are, however, so many exceptions to this rule that it s not much of a rule at all.

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