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We also provide an R program which produces publication quality histograms displaying embedded Q-Q plots and p-values, to evaluate for normality of the read distribution and statistical significance, respectively.
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The underlying distribution for all variables was evaluated for normality and variables with a non-normal distribution (TP, LF power, HF power and the LF/HF ratio) were transformed with the natural logarithm in order to obtain normality prior to analysis.
Data sets were evaluated for normality using the D'Agostino-Pearson test and by examining normal plots.
Data obtained from each experiment were first evaluated for normality and variance homogeneity, followed by the analysis of variance (ANOVA).
Data obtained were first evaluated for normality and variance homogeneity, followed by the analysis of variance (ANOVA).
Pain score data was first evaluated for normality of the distribution and subsequently dichotomised into two categories based on the 50th percentile of the distribution.
For statistical analysis, data were evaluated for normality, equal variance and sphericity, and analyzed by one-way repeated-measures analysis of variance with multiple dependent measures (MANOVA).
Data resulting from quantitative measurements were treated following GLM procedures, evaluated for normality distribution, and linear correlation coefficients were calculated after logarithmic transformation (Snedecor and Cochran 1957), and tested for significance.
Data were evaluated for normality.
For all regression models, studentized residuals were evaluated for normality and logarithmic transformations of the dependent variable was performed to improve normality.
Data was evaluated for normality and homogeneity of variance and a factorial ANOVA was performed to test for differences in DNA damage using R version 2.8.1 [55].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com