"model fitting results" is a grammatically correct and commonly used phrase in written English.
It refers to the outcome or findings of a model fitting process, which is a statistical method used to determine the best fit for a set of data. An example of using this phrase in a sentence could be: "The model fitting results showed a strong correlation between income and education level."
The pseudo-first-order kinetic model provided a good approximation (R2 > 0.99) to the measured adsorption data, and the model fitting results revealed that the MCDI with the ACF separator delivered not only the highest desalination rate but also the largest adsorption capacity.
Figure2 depicted the model fitting results of four patients.
The model fitting results are shown in Table 4.
Table 2 presents the model fitting results (to keep the paper short, the results on the submodels are shown only for the models with one dropped smooth effect).
As reported in the study, for large data size, model fitting results obtained from the Bayesian and the conventional probit model have no significant differences.
When the two outliers were carefully examined, the stability results of these two compounds had the following two common characteristics: 1) degradation kinetics from the isothermal experiments did not follow first order kinetics; 2) the non-isothermal degradations showed poor model fitting results (R2 < 0.999) causing the large errors in k 25 values as shown in Fig. 8.
Model-fitting results showed that additive genetic factors and the individual specific environment contributed to variation in all three dimensions.
Akaike information criterion (AIC) was used as the criterion to compare model-fitting results in the model selection algorithm.
The model-fitting results are reported in Table 2; this shows that close matches to each of the three patient datasets were achieved by the simulator.
Note that because these analyses require three composite measures at two different ages we focus on the model-fitting results, rather than the multiple regression, because the structural equation model does not require all of the measurements to be non-missing and therefore can use all available data.
If we re-standardize the g-independent genetic and environmental components of variance on the basis of this total g-independent variance of.70, the resulting ACE estimates are similar to the univariate model-fitting results reported in Table 4 for achievement phenotypically independent of g.
