Sentence examples for model test statistics from inspiring English sources

Using the maximum likelihood estimates of the parameters in the binary logistic regression model were used to determine the factors influencing the consumption of organic vegetables, the result indicated that the R square increased with the addition of the independent variables, while the regression model test statistics indicated that the model is highly significant.

However, mixed model test statistics are prone to increased type I error rates, particularly if utilized with large amounts of missing data.

This is not too surprising since mixed model test statistics are based on asymptotic approximations, and others [13], [14] have raised concerns about inflated type I error rates when using these tests.

Consequently, it is important to control for the non-independence of responses gathered from the same clinical area via performing analyses that address the multilevel nature of the data in order to obtain accurate model test statistics and scale reliability estimates.

Differences in the control of type I errors between models were also assessed by calculating for each trait and model, the test statistics of the Kolmogorov Smirnov test for deviation of the P-values from a uniform distribution.

The innovation of EIL is to show that instrumenting (widehat{u}_{t - 1}) and any other nonstationary covariates in the model yields test statistics that are asymptotically normally distributed.

Under the standard neutral model, both test statistics are expected to give values close to zero.

The third Section discusses grid maximum likelihood estimation and grid computing for the ordinal logistic model assumption test statistics.

However, because of the differences between modelling algorithms, methodologies and patterns of relative suitability, comparing models using test statistics and selecting a 'best' model are not recommended.

The simulation results from linear regression show that even mild heteroskedasticity or mean-model mis-specification can inflate model-based test statistics.

The significant variables selected to build the additive linear regression model in men, the estimate terms for the model and corresponding test statistics are provided in Table  3.