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Analyses (Oneway, Contingency) and statistics were produced using JMP 10 (SAS) software, including data distribution and variance analyses between groups.
Data were analyzed using one-way analysis of variance (ANOVA) followed by post hoc Tukey's test for groups with the normal distribution and variance homogeneity.
We provide computational results that show the effects of the distribution and variance of demand.
Since a normal distribution and variance homogeneity of the data could not always be assumed, analyses were exclusively performed using non-parametric statistics [35].
Non-parametric analysis was performed (Mann-Whitney or Kruskal-Wallis followed by Dunn's Multiple Comparison Test) when assumptions on the normal distribution and variance did not allow otherwise.
Neither homoscedasticity nor normality nor balanced group sizes are assumed, thus allowing for multiple comparisons in balanced and unbalanced models with arbitrary error distribution and hence arbitrary data distribution and variance structure.
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Measurement results showed that measurement values were not in normal distribution and variances between independent variables were not homogeneous.
We checked the normality of distributions and variance of all samples with the Kolgomorov Smirnow test (Köhler et al. 1996; Lozán and Kausch 1998).
In the absence of replicates, the intensity distributions for individual genes (and, therefore, the distribution mean and variance are unknown.
These factors accounted for 52% variance for plant distribution and 59% variance for AMF distribution.
In Step 2, we compared the signals extracted by the four methods in terms of temporal distribution, mean and variance.
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distribution and disparity
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