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The multivariate normality assumption was confirmed by normal quantile-quantile plots of residuals.
Multivariate normality was assessed by calculating the normalized estimate of Mardia's multivariate kurtosis coefficient [ 24].
We conducted Mardia's normalized coefficient of multivariate kurtosis to examine multivariate normality.
Examination of univariate and multivariate normality for each questionnaire item was highly suggestive of non-normal distributions in the population.
However, all are sensitive to departures from multivariate normality.
Korkmaz, S., Goksuluk, D. & Zararsiz, G. MVN: An R package for assessing multivariate normality.
The analysis of such multivariate data is usually based on MANOVA models assuming multivariate normality and covariance homogeneity.
In testing for multivariate normality of monotone incomplete data, we construct Mardia-type statistics for testing kurtosis and skewness, and derive their asymptotic distributions.
Most of the existing bulk-density pedotransfer functions (PTFs) are formulated as linear (multiple) regression functions, with the requirements for multivariate normality and homoscedasticity.
Significance testing of multivariate models and their factors (Supplementary Table 3) was done via permutation tests (1000 permutations), an approach robust to deviations from multivariate normality and variance homogeneity; p < 0.05 was considered significant.
The multivariate normality assumption was also retained.
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