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In Studies 1 and 2, results of confirmatory factor analyses showed that the fit indices for the 21-dimension model were poor and that seven items displayed low factor loadings.
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Although the numerical performance of the model is poor, the results it picks seem intuitively pretty good.
If the F value is small, the fit of the model is poor.
The carbon and sulfur models were fair and oxygen model was poor.
Further, the fit of data to the intraparticle diffusion model was poor (R 2 ≤ 0.11).
All these ratios indicated that model is poor fit with data.
However, the predictive capabilities of the model are poor, especially for apparently chaotic behavior.
Because rainfall data are not available in advance, the forecasting accuracy of our Indian rice acreage model is poor.
The quality of the model was poor in vegetable samples classification according to location (see Additional file 2).
The information contained in the first model was poor, whereas the second model had a higher predictive value.
The Bayesian two-stage approach to design experiments for the general linear model when initial knowledge of the model is poor, is reviewed and extended.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com