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Since there were no rewards in Session A, data from the Session A was used twice to plot for the two reward conditions in Session B. Overall, the majority of the subjects spent more time on the first target in the training session as most points fell between 0.5 and 0.7.

In facilities with option A, data from pre-MBP periods were compared to data collected in the same facilities  after introduction of MBP (Cohort 3).

This year was chosen to achieve comparability with the Medicare Part A data from the Dartmouth Atlas of Healthcare, provided by the Dartmouth Center for the Evaluative Clinical Sciences.

This wide variability in air benzene concentrations shown in Figure 1(A) (data from Table 4, HEI, 2007) is due to many factors such as sample location (e.g. rural versus urban; outdoor versus indoor), season and time of measurement (e.g. winter; afternoon), number of observations, average sampling time and other factors (e.g. mean versus maximum concentrations).

Description: Digital images of file slips summarizing (a) data from Golla's field notebooks and (b) archival data collected by Edward Sapir.

Trend patterns and check for stationarity were demonstrated for a data from a target site based on both non-parametric Mann Kendall and parametric regression tests.

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This result was obtained by comparison of SNP-A data from both granulocytes and sorted CD34+ blasts (Figure 3D, E, F).

* = significant difference (p < 0.05) to COM-group in a multivariate logistic model, a = data from PEK II and b = Fisher's Exact Text c = 95% Confidence Interval Table 2 shows socio-demographic data, the self-rated health status of the participating patients, their reasons for consultation and their expectations.

These effects are tested in 20 hypotheses on how ownership dispersion affects M&A, with comprehensive M&A data from the 1990s and 2000s, and a new detailed hand-coded matched sample of 120 recent public and private target M&A contracts.

The survival curve is shown in Figure  3 Panel A. Data from this negative study were used to develop a clinical prediction model for mortality based on bilirubin and albumin levels, prothrombin time, presence of edema and age at diagnosis [ 11].

Finally, accuracy of the clinical algorithm was evaluated by applying it to stage I patients from Validation Series A. Data from Training Series B (n = 88) were analyzed to select genes associated with outcome (DSS) in the clinical setting of ACT-treatment.

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