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This variance to mean power function exhibited another property of note – scale invariance.
The variance to mean power function seen in Fig. 2 would be a direct consequence of this model.
Indeed the variance to mean power function was evident with both of these alternative definitions (data not provided).
As will be seen below the variance to mean power function implicated a specific probabilistic model to represent the distribution of gene structures along chromosome 7.
A variance to mean power function inherent to this clustering implicated a scale invariant PG distribution to describe the spatial distribution of genes within the chromosome.
Larger deviations were seen between this relationship and the data, relative to those seen with the variance to mean power function.
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That means the power function relationship is the best fit and robust for all scenarios.
We found that the spatial variability of groundwater storage anomalies (deviations from the long term mean) increases as a power function of extent scale (square root of area).
Allowing a power function means that the model could indicate a slight increase in risk in moderate malnutrition, with an abrupt increase in risk with severe malnutrition, if that was a better fit for the data.
The strong linear relationship between the logarithmically transformed variances and means indicated that the variance and the mean were related by a power function, var(Z) = a·E(Z) p, where a and p were constants.
The within-group heteroscedasticity was modelled as a power function of mean fitted values.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com