Sentence examples for model explanations are from inspiring English sources

According to that model, explanations are arguments showing that the event to be explained (the explanandum event) was to have been expected on the basis of laws of nature and the antecedent and boundary conditions (the explanans).

Our model explanations are not exclusive and there are alternative ways to explain the phenomena of varying Nanog levels.

Determinants of priority scores and their contribution to the model explanation were assessed with the general linear model [ 11], with the priority score the dependent variable and the variables in the algorithm the covariates.

However, even if these covariates were significantly associated with HRQoL 30 days postoperatively, its contribution to the model explanation was minor and did not have any appreciable impact on HRQoL.

Study of the validity of the explicit criteria: Determinants of appropriateness scores and their contribution to the model explanation were assessed with the least-squares regression model[ 10], with the median of the panelists' ratings being the dependent variable for each indication, and the variables in the algorithm being the covariates.

Models were validated with Shapiro.test function (residues normality) in R Stats package and hmctest or bptest functions (heteroskedasticity) in lmtest library (http://cran.r-project.org/web/packages/lmtest/index.html) for R. Models explanation was done with the anova function of R Stats package.

Existing other models and explanations are not comparable on this point (No violate the nature and rule of mitochondrial replications).

In this formalization, models are defined as sets of model fragments, causal explanations are generated using causal ordering, and model simplicity is based on the intuition that using more approximate descriptions of fewer phenomena leads to simpler models.

Students' models and explanations were videotaped but instructors did not attempt to correct mistakes during this exercise.

(IV) Experimental validation: model predictions together with biological explanations are integrated to guide the design of new experiments, which in turn validate or falsify the model.

One connection of our model with existing explanations is the concept of assimilation and contrast in perceived brightness.