Sentence examples for because models can be from inspiring English sources

Substitution models are intended to correct these problems [33]; however, because no model summarizes the substitution process perfectly, and because models can be estimated inaccurately [30], any correction for multiple substitutions will also be imperfect [34].

This is because the models can be refined by providing such details in the further refinement of the model.

In science, it is essential to model phenomena because these models can be used to explain behavior, thus allowing predictions to be made [2].

Because such models can be generated from ordinary photographs taken with standard cameras in ordinary lighting conditions, these techniques are revolutionising digital recording and analysis in archaeology and related subjects such as palaeontology, museum studies and art history.

Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions.

Because regression models can be severely affected by outliers, we removed these two counties and used only the remaining 103 units in the study area in the following analysis.

This is perhaps unsurprising, because NF1 models can be created by gene knockout, whereas other RASopathy models require the introduction of a specific mutation into the endogenous locus, which is technically more challenging to achieve.

Because PE models can be useful to assess potential public health impacts from VOCs and to assist in the development of environmental policies to reduce human exposures to and risks from VOCs, it is important to know how well exposure models estimate PE.

This simplifies the process of assessing uncertainties about soil data because characteristic uncertainty models can be defined for each data type.

As Brown (2006) noted, the use of the FA model provides greater analytic flexibility than the IRT framework because traditional IRT models can be embedded within a larger model that includes additional variables to explain the item parameters as well as the latent trait (e.g., Ferrando et al. 2013; Glöckner-Rist and Hoijtink 2003; Lu et al. 2005).

Because estimates from multivariable models can be less robust when many factors are included, we also used propensity score matching.