Sentence examples for testing for model from inspiring English sources

A paired t testing for model validation was performed using the Python programming language (Version 2.7.8, Python Software Foundation, Wilmington, DE, USA, https://www.python.org).org

Unlike prior reviews, we evaluated reporting of additional model characteristics in depth including testing for model fit, evaluation of convergence, adjustment for covariates or multiarm trials, the specific priors used and availability of the code and aggregated study-level data.

Testing for model differences between groups requires multiple sample analysis, which in the case of gender, for example, would reduce the subsamples to n = 94 and n = 80, providing inadequate power for the number of free parameters in some of the models.

While prior publications focused on the evaluation and reporting of assumptions made within the models, we evaluated additional model characteristics in depth including testing for model fit, evaluation of convergence, adjustment for covariates or multiarm trials, the specific priors used and availability of the code and aggregated study-level data.

The main advantages of the latter approach are: 1) identifying and testing for model fit, 2) flexibility of estimating the factor intercorrelations, 3) enabling to fix parameters to certain values, 4) relaxing parameter values to be free and 5) enabling comparison of factor structures (in this study comparison of the two measurement moments).

Users select input functions at run time for testing numerical algorithms for correctness (verification testing), for model exploration (behavioral analysis) or for analyzing data as for steps 6 and 7 in our "13-Step" process.

These statistics were also used to test for model invariance across the cohorts.

(4) Lastly, we test for model stability and residual analysis to check for normality, autocorrelation and heteroscedasticity of the models residuals in the fourth step.

Following model generation, a multiple-groups analysis was conducted to test for model structure invariance across the three cohorts of students.

AUROC was performed to test for model discrimination.

We tested for model goodness-of-fit using chi-square test which showed that the model fit the data well.