Sentence examples for common regression model from inspiring English sources

The relative residuals (absolute residuals divided by the predicted values) from this common regression model were then compared in a Nested ANOVA with foraging niche category (original subterranean – leaf-litter – surface swarm raiding – reversal subterranean) as a fixed factor and species as a random factor nested within foraging niche category.

The massive enrichment of the right tail of the BMI distribution implies that the data cannot be analysed with BMI or BMI-associated outcomes as response variables in common regression models.

Thus, the massive enrichment of the right tail of the BMI distribution implies that the data cannot be analysed with BMI or BMI-associated outcomes as response variables in common regression models.

In all this work, the effect of covariates on hazards is multiplicative, as is usually performed for common regression models (general linear model or Cox model).

When using common regression models for censored data, there are three possible approaches for the estimation of the transition hazards: separate models for every transition (approach S), time-dependent covariates (approach J) [ 12], and the stratified model [ 13].

We investigate the D-optimal design problem in the common trigonometric regression model, where the design space is a partial circle.

The problem of determining optimal designs for least squares estimation is considered in the common linear regression model with correlated observations.

In the common Fourier regression model we investigate the optimal design problem for the estimation of linear combinations of the coefficients, where the explanatory variable varies in the interval.

It is shown with the application problem considered that the Kriging interpolation models are efficient surrogate models for structural reliability problems and can provide significantly more accurate failure probability predictions as compared with the most common polynomial regression models.

While the results of the re-survey suggest that provider timing assignment errors in the principal provider sample were not common, all regression models using the principal provider sample were re-estimated using a restricted sample of providers with a confirmed practice affiliation for the pre- and post-MRI years.

The right part of Figure 1 illustrates a combination of common factors and regression model.