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Using a pretest posttest single group design and hierarchical linear modeling, the results of this study analyze student achievement as measured by standardized test scores.
To optimize the modeling, the results of the algorithms were compared by minimized root mean squared error (RMSE) and the percentage of absolute average deviation (AAD) while the coefficient of determination (R2) was maximized.
While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling; the results are inconclusive for software cost/effort/size estimation.
Using structural equation modeling, the results showed that the higher the NPI-S is, the more often students asked to teacher, were reprimanded from teacher, and got good grade.
Combined with microscopy and computational modeling, the results begin to yield a more coherent picture that integrates linear and three-dimensional (3D) views of chromosome organization in relation to gene regulation and other nuclear functions.
Although 30 points data set can be considered as small for the neural network modeling, the results were quite satisfactory apparently due to the fact that the experimental data generated with response surface method were well balanced over the experimental region and it was very suitable for neural network modeling.
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When immunosuppressive use was additionally modeled, the results were similar to the model without medication use.
When he applied the index to the G.F.D.L. model, the results were even more dire.
And as with all models, the results are estimates.
In the mesothelioma model the results were less pronounced.
Despite many attempts to refine this model, the results were dismal.
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