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In this paper we report the results and also discuss the implications of the observations on model development and design.
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The following discussion is necessarily speculative, since we are trying to apply observations on isolated and model whiskers to a consideration of animal behavior and evolutionary advantage.
This is reasonable for assessing the impact of observations on dynamo model solutions, but is expected to be insufficient for providing SV forecast to IGRF.
The number of observations on which models were based varies depending on the pattern of missing data by pollutant, lag, and season.
Moreover, our results are supported by observations on murine models, given that mice with mutations resulting in under-expression of the clock gene presented obesity and hyperglycemia [ 32, 33].
Leverage plots and partial regression plots were examined to assess any influence individual observations might have on model coefficients.
This observation bears on models of magma rheology, but is not considered further here.
The influence of each experimental observation on the model parameter estimates is then quantified using the Cook distance and DFBETA measures.
The clinical trial result is highly consistent with our observation on mouse model, and making it a solid basis for future large-scale clinical study.
Briefly, robust regression is a model-fitting procedure that limits the influence of statistical outliers (extreme observations) on the resulting model.
We discuss the constraints imposed by these observations on the correct model of long wavelength dynamics of ion sputtered surfaces.
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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