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Quantitative data were reported as mean ± standard error regardless of the distribution of the variables.
We first used the Shapiro-Wilk test to assess the distribution of the variables.
Kolmogorov Smirnov test was employed to establish the normality of the distribution of the variables.
The Kolmogorov Smirnov test was used to analyse the normal distribution of the variables (P > 0.05).
Parametric or nonparametric statistics were used depending on the distribution of the variables.
Normal distribution of the variables was evaluated with Kolmogorov-Smirnov tests.
Kolmogorov-Smirnov tests were applied to verify normal distribution of the variables.
Because the distribution of the variables was non-normal, the maximum likelihood estimation with robust standard errors (MLR) was used.
Fewer maps or diagrams were needed for illustrating the spatial distribution of the variables or parameters and relationship between them.
Before the application of the normality tests, we analysed the distribution of the variables by constructing the boxplots.
The results show that the cLHS is the most effective way to replicate the distribution of the variables.
More suggestions(12)
distribution of the factors
distribution of the variances
distribution of the characteristics
distribution of the measures
distribution of the fluctuations
distribution of the determinants
distribution of the variations
distribution of the varying
distribution of the variance
proportions of the variables
ranges of the variables
distributions of the variables
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