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The best models that described the adsorption data were determined by looking at the values of the R2 and two error functions which are the sum of absolute error (EABS) and Chi-square test (χ2) as shown in Eqs.
To investigate the stability and potential reproducibility of the model (using all candidate predictors) we constructed 10 "best models" that included 10 variables.
As shown in Table 1, three variables (atmospheric pressure, maximum and minimum relative humidity) were found in the best models that predict for the week of the outbreak and one week in advance, but were less likely to be found in models that predict outbreaks two and three weeks in advance.
The three temperature (minimum, average and maximum) variables are similarly represented in the best models that predict for the week of the outbreak and one week in advance, however, minimum temperature is more frequently included in the models that best predict the outbreak two and three weeks in advance.
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It is found that GP evolves the best model that is able to predict open porosity satisfactorily based on given values of laser power.
In addition, Model C2 and Model D1 outperform the best model that estimates both exogenous variables (Model B4) with 11.9% and 9.3% compared 30.4% over the first data set.
Abraham's model [ 51] is the best model that is currently publicly available.
> -wrap-foot> In mixture modeling, statistical theory determines the best model that combines goodness of fit and parsimony through minimum values for information criteria.
In summary, we have shown that the best model that predicts grade 2/3 VFs comprised the number of years since menopause and the BMD of the lumbar spine.
Backward logistic regression analysis was performed to identify the best model that explains a B-Pb of 100 μg/L or above.
The best model that predicts grade 2/3 VFs comprised the number of years since menopause and the lumbar spine T-score.
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