Sentence examples for both models were good from inspiring English sources
The goodness-of-fit tests suggested that both models were good fits.
The Ramachandran dihedral statistics [49], [59] for both models were good; eEF1A1: 93.6% most favored, 5.1% additionally allowed, 1.1% generously allowed, 0.3% disallowed; and eEF1A2: 92% most favored, 6.7% additionally allowed, 1.3% generously allowed, 0% disallowed.
The results show that both models are good for low values of impedance, despite of the overall sub-estimation of the acoustic impedance by the petrophysical model.
Power for both models was good with a sample of size of 250 and a small to medium effect size.
Conclusions: Both CRASH and IMPACT prognostic models were good in discriminating between favourable and unfavourable long-term neurological outcome for patients with severe TBI requiring decompressive craniectomy, but the calibration of the IMPACT full-model was better than the CRASH model.
The discriminative ability of the Framingham and SCORE models were good (area under the curve > 0.85).
These models were good-fit models based on the large-scale dataset (HL statistics < 15.0).
The goodness-of-fit of both multivariate models was good, with a p value of 0.899 (Hosmer and Lemeshow test) for the model predicting pneumococcal pneumonia and a p value of 0.995 for the model predicting pneumonia of an atypical aetiology.
Moreover, both Wilke and Wilke Bosanquet models are good replacements for the more rigorous Maxwell Stefan and dusty gas models.
High coefficient of determination (R 2) values ( >99%) showed that the prediction ability of both the ANFIS and RSM models was good enough for the response when the interpolation ability of the models was considered.
