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Версія від 06:10, 30 червня 2017, створена Iranchild1 (обговореннявнесок) (Створена сторінка: 2a) that this last variable was the first one next to the top of the tree. Again, the testing issue, either expressed only as ��tested�� and ��untes...)

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2a) that this last variable was the first one next to the top of the tree. Again, the testing issue, either expressed only as ��tested�� and ��untested��, or expressed in annual frequency, was systematically the most explanatory. diglyceride Overall, the patterns found in the CHAID diagrams and the other statistical tests were quite similar. The analyses of the three occupational risk groups corroborated the relevant findings for the whole sub-population, with an exception in group 2 (working near or around the trains), for which the best explanatory variable (most significant in CHAID diagram) was ��Occupational risk sub-group�� (Chi-square?=?17.035; p-value?GDC 941 all, being subjected to tests was also associated with the response variable, although with a lesser strength of association. Furthermore, some classes of ��Annual testing frequency�� were of low dimension (below 30 employees), due to the smaller size of group 2 itself (N?=?318). Thus, it was verified the existence of associations between occurrence of accident victims and previous testing frequency �C at 1% significance level, with a small difference in group 2, for which the significance level was 5%. Additionally, the strength of these associations was measured by Cramer V coefficient �C whose values allowed concluding that they are moderate in the whole sub-population (0.270), as well as in groups 1 (0.226) and 2 (0.247), and strong in group 3 (0.364) ( Healey, 2010, Le Roy, 2012?and?Murteira, selleck kinase inhibitor 1990). Once the expected negative association between accident occurrence and prior tests was confirmed, this study focused on the annual test frequency and accident rates �C both annualised variables. The objective was to identify how far an organisation should go in terms of testing effort. The interest was to find out the optimal frequency, above which there is no benefit in increasing testing, i.e., the frequency of tests at which the accident rates are minimised. In Fig. 2a, Fig. 2b, Fig. 2c?and?Fig. 2d, one can see that, among the statistically different classes of testing frequency, one of them reveals itself as the one with less accident occurrence after A&D testing. The authors have interpreted this pair as an optimal frequency. To clarify this result of primal importance, the set of Fig. 3a, Fig. 3b, Fig. 3c?and?Fig. 3d depict the sub-population (3a), group 1 (3b), group 2 (3c) and group 3 (3d). These graphical representations are more intuitive than CHAID diagrams, showing the same reality by annual test frequency intervals, but using frequency classes easier for realistic implementation. In these figures, F?=?0.0 stands for zero tests per year per worker, equivalent to not-tested �C this was the class used for control.