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passband response variance.
For the family scale, the model was able to explain 30% of the response variance.
Genetic effects accounted for 12 60% of the observed response variance.
Along the bar a drop of the mean value of the response with the simultaneous increase of the response variance have been observed.
A similar approach is taken to distributing response variance across the various sources of deviation from the mean in classical analysis of variance (ANOVA).
While accumulating evidence suggests that variants of several genes account for some of the observed response variance, the relative contribution of these factors remains unknown.
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In order to test for difference in the variability of response, variances of tested parameters observed in each treatment group were compared using Wilks test [ 22].
For widely distributed parameters, we see that both methods tend to minimize the amount of stimulation, as this results into little response variances at maximal distances between expected model responses.
It is not possible to determine whether response variances are homoscedastic (constant) or heteroscedastic (not constant) in unreplicated data sets, and few degrees of freedom may be available for statistical testing after curve fitting and outlier detection.
Three examples with different features in terms of responses variance are used and the performance of various analysis methods is compared.
We call this form of VarP the diallel treatment-response variance projection (TReVarP).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com