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Likelihood ratio models for physicochemical data are usually constructed with the use of kernel density estimation procedure that exempts from the need for parametric distribution choice.
' t-test for parametric distribution.
The independent t-test was used for parametric distribution.
Furthermore, a t-test (for parametric distribution) was used for testing the differences between both groups.
Variables were tested for parametric distribution by applying the Shapiro-Wilk Test (null hypothesis rejection set at p < 0.25).
Continuous variables were reported as the mean and the standard deviation (SD) for parametric distribution or as the median and the interquartile range (IQR) for non-parametric distribution.
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Here, we employ a scheme to efficiently divide the sampling areas for parametric distributions using both FCM and CVT.
Here, we propose an approach to efficiently divide the sampling area for parametric distributions using Fuzzy c-means clustering (FCM) and Centroidal Voronoi Tessellation (CVT) diagrams.FCM has frequently been used in the past for the classification of numerical data.
Continuous variables were reported as mean values and standard deviations (SD) for parametric distributions or as median values and interquartile ranges (IQR) for non-parametric distributions.
Tools necessary for the statistical analysis of CAP data are also being developed and will be used for the generation of parametric distribution models.
For the parametric distribution of the change point we estimate τ 0 and σ.
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