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The statistical Leverage analysis was employed for the examination of the outlier and doubtful data.
This paper builds on a framework for formal verification of architectural requirements, either from a qualitative or quantitative (probabilistic) point of view, which will leverage analysis and adaptation prediction.
The leverage analysis revealed that these findings were not attributable to the influence of individual ICUs.
DOI: http://dx.doi.org/10.7554/eLife.02743.010 10.7554/eLiFigure43 figuregure 3—figure supplement 1. Leverage analysis for stimulus response correlations.
To overcome the potential bias of the small sample of units, they used a leverage analysis.
Leverage analysis was carried out on the refinement of l-alanineB using CRYSTALS (Parsons, Wagner et al., 2012 ▶).
To ensure that the observed correlations/regressions were not spurious results generated by a few outliers, we performed a partial leverage analysis.
The confidence intervals of the parameters in the full model based on the leverage analysis were comparable to the intervals given in Table 4.
A leverage analysis was performed for each of the variables that showed a significant relationship with outcome, to detect whether these results were caused by one single ICU.
Despite the leverage analysis showing that their results were not due to data from one specific unit, given the relatively small sample of units in the study, the possibility of a group of units biasing these results still exists.
Finally, the leverage analysis revealed that similar results were obtained based on the jack-knife estimates and that none of the findings were attributable to the influence of individual ICUs participating in the study.
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