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Parametric datasets were compared using a two-tailed, Student's t-test, paired or unpaired depending on the experimental setup.
The target systems are modeled on the basis of system parametric datasets, and their motions are specified and graphically represented according to characteristics of applied control algorithms.
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A parametric dataset was created from DIFF by setting the signal in voxels belonging to Cl3 and Cl4 to zero.
Non-parametric datasets were compared using a Mann–Whitney test.
Significant differences between experimental groups were analysed by using a t-test modified for small samples for parametric data or Mann-Whitney U test for non-parametric datasets.
To compare outcome parameters between groups, an analysis of variance or the Kruskal-Wallis test were applied for parametric and non-parametric datasets, respectively.
Statistical analysis was performed using an independent t-test (or Mann Whitney U test with non-parametric datasets), or one-way or two-way ANOVA as appropriate, with Bonferroni post-hoc tests.
Parametric bootstrap datasets were generated using the parameter estimates from the illustrative datasets as parameter values.
Seq-Gen [ 80] generates 100 parametric bootstrap datasets under the RAS model using the PHYML-generated parameter estimates, and the W statistic is computed for each.
In addition to considering the data sampled using GPR bootstrapping, we also performed a non-parametric bootstrap of the data, and calculated a sampling distribution over ρ (i) based upon 1000 (non-parametric) bootstrap datasets.
Yet, these studies were mostly descriptive due to inherent limitations of standard statistical analyses against large, non-parametric community datasets.
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CEO of Professional Science Editing for Scientists @ prosciediting.com