### Table 4 Descriptive Statistics and Inter-Item Correlations for Dependent and Scaled Independent Variables

2003

"... In PAGE 26: ... We estimated the probit model for the two independent variables, customized service (CSERV) and customized advertising (CADV), independently. The maximum likelihood estimates of the parameters of these models are presented in Table4 . Table 4: Maximum Likelihood Ordered Probit Estimators Variable CSERV (p-value) CADV (p-value) INFO_KNOWUSE -0.... In PAGE 26: ... The maximum likelihood estimates of the parameters of these models are presented in Table 4. Table4 : Maximum Likelihood Ordered Probit Estimators Variable CSERV (p-value) CADV (p-value) INFO_KNOWUSE -0.2861** (0.... In PAGE 27: ...across the negative parameter estimates. The third column of Table4 summarizes the results for the second model, where the dependent variable is willingness to be profiled online for customized advertising (CADV). Here, demographic control variables, previous privacy invasion (PREV_INV), and general concern for online privacy (PRIV-CONC) are significant.... ..."

### Table 2: Descriptive statistics of ratio scaled independent variables Variable Max 75% Median 25% Min Mean StdDev

### Table 3: Contributions from vector (V ), axial vector (A), scalar octet (S) and scalar singlet (S1) resonances to the couplings Z and K1 : : : K14 at the scale point 0 = MV apos; M . We have used the relations in Eq.(14) and the numerical values for the parameters given in Eqs.(15,17). Scale independent quantities di erent from zero are signed with an asterisk ( ).

in Resonance Contributions to the Electromagnetic Low Energy Constants of Chiral Perturbation Theory

"... In PAGE 30: ...KS 1 ( 0) = 34 1 16 2 c2 d F 2 0 ln M2 S 2 0 + 16 ! KS 2 ( 0) = ?KS 1 ( 0) KS 3 ( 0) = ?13KS 1 ( 0) KS 4 ( 0) = ?23KS 1 ( 0) KS 5 ( 0) = ?KS 1 ( 0) KS 6 ( 0) = KS 1 ( 0) : (66) Singlet : KS1 3 ( 0) = ?3 2 1 16 2 ~ c2 d F 2 0 ln M2 S1 2 0 + 1 6 ! KS1 4 ( 0) = 2KS1 3 ( 0) : (67) The numerical results at the scale point 0 = M (with the values for the paramet- ers given in Eqs.(15,17) ) are presented in Table3 . There are no contributions to K7 : : : K10 from the resonances at all.... ..."

### Table 1. Prediction errors (NRMSE) for the data using the Bayesian Network in Fig. 2

"... In PAGE 9: ... 1, and run inference on the second half of the data. Table1 shows the normalised root mean square error (NRMSE) of the inference. NRMSE gives a useful scale-independent measure of error between data sets of different ranges.... ..."

### Table 3a - European Research Area R amp;D Intensity (2003)

"... In PAGE 15: ... Table3 b - Canadian R amp;D Intensity (2003) Rank Province Traditional Province Scale- independent 1 Quebec 2.72 Quebec 1.... ..."

### Table 3b - Canadian R amp;D Intensity (2003)

"... In PAGE 15: ... Table3 a - European Research Area R amp;D Intensity (2003) Rank Country Traditional Country Scale- Independent 1 Netherlands 5.35 Netherlands 1.... ..."

### Table 5. Abundances with respect to Morrison amp; McCammon (1983)

1997

"... In PAGE 10: ...as a factor 1.5 lower iron abundance (see section 5.2), in both the plasma and reflection codes. Details of some of these ts are given in Table5 . We rst allow the line to scale independently of the reflection continuum, and the re- flection spectrum abundances to scale independently of the hot plasma.... ..."

### Table 2: Five experiments for 1; 000 simulations in the layered media in func- tion of L

"... In PAGE 18: ... We set m5 = (t1 + + t5)=5 and 5 = sd(t1; : : : ; t5). In Table2 , the dependence of L is studied. In fact, our algorithm is scale- independent, and the results behave as expected when L increases.... ..."

### Table 1. Characterisation Vector

2000

"... In PAGE 2: ...ts operations, its extra registers (i. e. not pipeline registers), its inherent struc- ture, and the number of iterations it is run through. The Characterisation Vector shown in Table1 represents the characteristics of an algorithm, whereby several... ..."

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### Table 1. Comparison of characterisations

2001

"... In PAGE 35: ...We now try to provide an intuitive understanding of the technical differences between the characterisations of termination we have proposed. These are sum- marised in Table1 . Note that simply-acceptability is a special case of P-simply- acceptability that does not need to be distinguished in this context.... ..."

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