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The residuals were used to check the homogeneous variance assumption by plotting the (studentized) residuals against the predicted probability values.
At a significance level of 0.05, the Breusch-Pagan test (p value = 0.24) could not reject the hypothesis that the residuals have a homogeneous variance, and the Durbin-Watson test (p value = 0.26) also could not reject the hypothesis that the residuals are independent.
Model fit was assessed using Akaike's Information Criterion (AIC) assuming the observations are drawn from a normal distribution with mean γ i and homogeneous variance.
For microarray data, expression intensities for each probeset were computed as MAS5 signal intensities, and subsequently quantile-normalized and transformed to homogeneous variance using the "S10" transform available in the MSCL Analyst's Toolbox (http://abs.cit.nih.gov/MSCLtoolbox/).nih.gov/MSCLtoolbox/
This variable was normally distributed with homogeneous variance.
The model assumes homogeneous variance (ie, τ jk = τ).
Parametric tests were used to analyze variables with homogeneous variance and normal distribution, whereas non-parametric tests were used to analyze variables without homogeneous variance and normal distribution.
$Analysis of covariance for variables showing both normal distributions and homogeneous variance; Kruskal-Wallis test for variables showing normal distributions and/or homogeneous variance.
The gene-level summary measure is also assumed to have a homogeneous variance.
Antibody concentrations were log transformed to obtain normally distributed residuals with a homogeneous variance.
Thus, it would be inappropriate to assume the homogeneous variance assumption for the gene-level measures.
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