Sentence examples for lack of normality from inspiring English sources

Because often this assumption is not fulfilled, researchers normally approach phenotypes in three ways: (a) they ignore the lack of normality in the phenotypes; (b) they transform the non-normal phenotype to approximate it to normality; or (c) they use generalized linear mixed models (GLMMs) to model the appropriate distribution of the phenotype (Stroup 2015).

This lack of normality may be attributed to the existence of two scoring subgroups (normal subjects and GAD subjects), assumed to originate from different reference populations.

Lack of normality in the distribution of detection scores, and the inability to adequately normalize these values led us to employ the Spearman Rank Correlation to determine the correlation between detection and our metrics of interest, as well as to examine the correlation between detection and key constraints (e.g. heritability).

Inverse distance weighting (IDW) method was used due to lack of normality of distribution of annualized average incidence and difficulty of transformation (into to normal distribution).

The generalized least squares extraction method was used in favor of maximum likelihood because of the lack of normality of the data.

For continuous variables we used the median (range) due to lack of normality.

Absolute values of skewness and kurtosis beyond 2 and 7, respectively, may imply a lack of univariate normality (Finney and DiStefano 2006).

Due to the lack of multivariate normality, it was difficult to use the chi-square distribution to interpret the (non-central) chi-square statistic, and although the RMSEA depends on the normality assumption as well, it is still possible to use the statistic to compare the fit of different models to the same data.

Bootstrapping is a non-parametric mediation analysis that is advantageous over other approaches, particularly with small sample sizes due to its reduced Type 1 error rates and lack of assumption of normality [ 52].

15, 22, 23 We analyzed the differences in HRQL between disabled and nondisabled subjects by logistic regression models, because linear regression modeling presented a problem of lack of linearity and normality in the scores of SF-36 dimensions that we planned to use as dependent variables.

Firstly the Box-Cox analysis optimises transformation on the basis of implicit assumptions with the model; namely normality, lack of heterogeneity in subclass variance, and additivity of model terms, and whilst the procedure has been used here in the context of mixed models rather than a fixed model in which it was developed the underlying principles behind the optimisation may be assumed to hold.