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It is also clear that the amount of data that will be needed to resolve each of the regions is still difficult to estimate due to the large differences between the parametric and nonparametric simulation results.
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What is the prediction difference between the parametric GBLUP and the semi-parametric RKHS models?
Differences between the non-parametric, continuous parameters assessed at study inclusion and at 12-month follow-up visit were compared by the Wilcoxon rank-sum test.
Where parametric statistics have been used there were no differences between the results of parametric and non-parametric analyzes.
Thus while the analysis was based on the assumption of normal distribution, by considering the Pearson coefficient, occurrence of any differences between the results of parametric and non-parametric analysis was also noted.
Nonetheless, as explained earlier in this article, while the analysis was based on the assumption of normal distribution, by considering the Pearson coefficient, occurrence of any differences between the results of parametric and non-parametric analysis was also noted.
While we expected outcome variables to be normally distributed, differences between parametric and non-parametric testing would alert us to cases where this may not be true.
This sort of case exposes one of many fundamental differences between the logic of non-parametric and parametric maximization.
As expected, considering the inclusion criteria, all clinical parameters of all groups had significantly improved from baseline at T1 (after the first month of diet), with no significant differences between the groups (non-parametric tests, p < 0.05) (Table 3).
The slight differences between the correlation coefficients for parametric and non-parametric analyses are mainly due to minimization of extreme data point effects by non-parametric analyses.
The t test was used when the differences between the quantitative variables with parametric distribution were analysed.
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