Sentence examples for multivariate normality but from inspiring English sources

Rotation theoretically depends on multivariate normality, but simulations with grossly non-normal data shows that ROAST is insensitive to departures from normality, agreeing with analogous results of Dørum et al. (2009).

The perMANOVA is robust to violations of multivariate normality but requires homogeneity of variances.

Some papers briefly discussed the multivariate normality of their data, but none discussed the data screening and transformation (i.e., skewness or kurtosis, continuous or discrete, and missing data).

We did this to account for the binomial distribution of our fitness variable (survival), which likely violates the assumption of normal errors and multivariate normality in least-square calculations [ 52, 53].

The multivariate normality assumption was confirmed by normal quantile-quantile plots of residuals.

Multivariate normality is theoretically a strong assumption, but we show later that our test procedure is robust against departures from normality.

It is best when used not only with small sample sizes but when assumptions of multivariate normality cannot be made, and when the concerned with the prediction of the dependent variable [69].

Multivariate normality was assessed by calculating the normalized estimate of Mardia's multivariate kurtosis coefficient [ 24].

The results reveal that the proposed method gives better results depending on whether or not the data set is multivariate normal even though multivariate analyses require checking the multivariate normality.

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.