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Besides, a model was built by mass balance and polynomial fitting for evaluating the continuously pre-harvesting cultivation mode.
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In addition, Yusof et al. [ 38] proposed a human, organization, and technology-fit (HOT-fit) framework for evaluating the success of health information systems by emphasizing a good fit among human, organizational, and technical elements of the system.
While these have been useful for designing systems under normal conditions, they are not fit for evaluating minima and peaks in utility demand.
The test for evaluating the fit of the model with conditional independence (goodness-of-fit test) proved to be adjusted (P value = 0.938 for model A and 0.836 for model B).
Criteria for evaluating the fit of models are proposed and approaches to assessing the utility of results suggested.
Then, the best model was selected and the 2000 and 2006 maps (Figs. 10, 11) simulated and Percent Area Match location and quantity have been used for evaluating the goodness of fit the simulated maps (Tables 7, 8).
Furthermore, there is no consensus on the goodness of fit criterion which is most suitable for evaluating the appropriateness of the distribution to a particular application [22].
The equation for evaluating the best fit model is as follows: chi^{2} = sum {frac{{(q_{text{e}} - q_{text{e,m}} )^{2} }}{{q_{text{e,m}} }}} (9 where qe,m is the equilibrium capacity obtained from the model (mg/g) and qe is the experimental equilibrium capacity (mg/g).
The C-statistic for evaluating the goodness-of-fit was calculated.
Residuals were not available for evaluating the goodness of fit because the model estimated the probability of scores and not a prediction of the scores themselves.
There are no established statistical methods for evaluating the goodness-of-fit of recurrent events survival models, and this is a topic of further research.
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