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To verify Remark 2, we now examine the P-DF design and T-DF design under different variance assumptions of the RE channels.
To illustrate this obvious matter of fact we simulated cost efficiency under different variance assumptions.
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After finding the nonlinear model that best fitted the horn allometry of O. taurus, we again used log-likelihood tests to select among different variance structures that would fulfill the assumption of homogeneous variance of residuals across the predicted values of the model.
We used the same approach to select among different variance structures in our models, in order to fulfill the assumption of homogeneous variance of residuals across the predicted values of the models.
To meet uniformity of variance assumptions, light intensities were loge transformed.
Though the assumption of all features having the same variance seems unnatural for any dataset as the different features should have different variance, it is consistent with the simple method of principle component analysis.
Data were checked for normality and constant variance assumptions.
Equal variance assumptions were based on Levene's test for equality of variance.
The samples for ART_Jac, ART_Man, Fixed_Weights were considered normal, according Table 8, but presented different variances, violating an ANOVA assumption.
Our chosen illustrative examples are of different size and complexity and yield heterogeneity estimates for which the homogeneous variance assumption was violated to an extend that impacted the findings of the MTCs.
However, the homogenous variance assumption is typically unrealistic because the heterogeneity variances are likely different across treatment comparisons [ 5, 6, 15].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com