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Two studies that assessed O2 onset kinetics by different modeling techniques, suggest an acceptable reproducibility of nonlinear regression and an algebraic method (Belardinelli et al. 1998; Sietsema et al. 1994).
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We have revisited these raw data and regrettably, none of these data sets were collected in a manner which we think permits application of the modeling techniques suggested.
Small changes in ligand structure can lead to large unexpected changes in activity yet it is often not possible to rationalize these effects using empirical modeling techniques, suggesting more effective methods are required.
The 3-D geological modeling technique suggested in this paper, named as Sealed Geological Modeling (SGM), is a powerful tool for constructing complex geological models for rock engineering projects that require numerical stress analysis.
Therefore many statistical and numerical modeling techniques have been suggested for the ϕ′ value.
The results suggest that for one system MARS can predict maintainability more accurately than the other four typical modeling techniques, and that for the other system MARS is as accurate as the best modeling technique.
The framework suggests various mapping schemes that help in establishing strong concordance among different artifacts of these modeling techniques and maintaining consistency of overall system architecture.
Two modeling techniques were used.
Before either hypothesis-testing procedures or regression-modeling techniques are pursued, we suggest so-called multiple contrast tests be carried out (Branson et al. 2003; Bretz et al. 2005) in order to guarantee that at least one tested dose has produced an effect that is significantly different from the controls while preserving the global error rate at the prespecified α.
Molecular modelling techniques have confirmed this result and suggest that up to 25% of S configured units spread randomly or in a stereoblock fashion are isomorphous with the R repeats of isotactic helix segments.
After the performance indices have been calculated, we will internally validate the model using bootstrapping techniques suggested by Moons et al 33 (see online supplementary appendix B, Table B).
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