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An acceptable level of misfit (i.e., matching gravity data uncertainty) can be achieved using the existing 3D geologic model and allowing geologically reasonable, but variable density values in the individual rock units.
At both KMH and TSU, there is reasonable, but variable, agreement between the data and both models, and from the visual comparison, a general statement which of the models fits the data better is not possible.
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In order to confirm that the imputed values are reasonable, each variable with missing data in excess of 8% was examined to identify variables with large differences between the measured and imputed.
In addition, the statistical analysis indicates that MLR model with reasonable variable selection (SPA-MLR) could exhibit a slight superiority toward PLS-1 in the system.
Under the local technological and economic constraints, farmers use water cautiously and obtain reasonable (yet very variable) productivities.
The proposed model has been tested numerically and yields reasonable results for variable stiffness values of the joint elements.
It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs.
Low work satisfaction, on the other hand, is a state which can be draining in the long run, and it is reasonable that this variable is associated with GP-certified sick leave.
Of variables that were highly correlated (phi coefficient > 0.4), the more reasonable variable was selected for the multivariable analysis.
Considering that a high training accuracy does not entail a high prediction accuracy, we developed a new feature selection algorithm called gradient method to find reasonable variable features across multiple classes.
For example, the missing at random assumption may be reasonable if a variable that is predictive of missing data in a covariate of interest is included in the imputation model, but not if the variable is omitted from the model.
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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