Sentence examples for multivariate normality is from inspiring English sources

Christensen and Lund [ 14] did not rely on equation (7) to derive H, but they computed the mean of the missing genotypes conditional on the observed genotypes as: However, this is valid only for multivariate normal variables, and multivariate normality is not a good approximation for the distribution of marker covariates.

When the data fits the model, multivariate normality is assumed [ 31].

It is also interesting to observe how well MANOVA performs when the assumption of multivariate normality is violated.

It has been suggested that maximum likelihood estimation with robust standard error should be conducted when multivariate normality is not observed [ 39].

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

Multivariate normality is also reasonable when testing the means of samples over many conditions, and when proper data transformation is used on the *-seq data, such as log-transformation, averaging signals over intervals, or quantile transformation to z-scores.

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

Statistical tests for multivariate normality are also reviewed.

Before discussing the results, it should be mentioned that the assumptions of univariate and multivariate normality were met.

The univariate normality assumption was checked with the one-sample Kolmogorov-Smirnov Test (SPSS) and multivariate normality was assessed with the Shapiro-Wilk goodness-of-fit test (using JMP Pro 11) on the distribution of the Mahalanobis distances.

Variables with a skewed distribution were transformed using quadratic or Log transformations and multivariate normality was tested using Henze-Zirkler's test [56].