We observed that these models were statistically better (Table 5) despite the statistical drawbacks that have been attributed to the current implementations of these methods [ 26].
All top models were statistically significant, with adjusted R2s ranging from 0.05 to 0.42.
The two models were statistically discriminated in order to measure their predictive capability for the hydrogen-rich syngas production.
In addition, both models were statistically compared to two correlations developed by Vogel and also Daubert and Danner.
In this research, enthalpy wheel performance data generated using established fundamental enthalpy wheel models were statistically analyzed.
With few presence-only data of rare species, Maxent models were statistically and ecologically significant and reliable to develop distribution maps with high predictive power.
When the coefficients of the ARMA models were statistically significant, the models proved to be both visually and statistically an improvement over the polynomial trend.
These models were statistically significant and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment.
Two models were statistically compared by the coefficient of determination, root mean square error and absolute average deviation, based on the validation data set.
Further t-test performed based on the target outputs from the ANN MLP and ANN RBF network shows that the models were statistically significant.
We found that both of the nonlinear models were statistically adequate and they provided complete insights into the complex nonlinear input output relationships.
