Sentence examples for modelled variables was from inspiring English sources

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We found that the strength of the contextual effect on mental health for all the modelled variables was significantly stronger in people who were economically inactive from permanent sickness or disability.

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Furthermore, appropriating the same transfer constants (amRNA, bprot) for all modelled variables is a simplification of the model which was done in an attempt to reduce the size of the parameter vector and hence the dimensionality of the parameter space that needs to be sampled.

The modelled variables were used for a classification of fuel types compatible with the continuous data.

This method deals with both economic objective and secure objective in one objective function and modelled variables are all flows, so it is very suitable for analysis for distribution system.

The log modelled variable is a spatially smooth covariate with no adjustment for the environment, so it cannot capture higher measurements at the roadside and lower background measurements.

The dynamics of the variables was modelled using linear mixed-effect models with a random intercept and slope and compared [23].

For each data subgroup, Spearman rank correlations were used to evaluate collinearity between variables prior to conducting meta-regression; if evidence of collinearity was identified (ρ ≥ 0.8), only one of the correlated variables was modelled based on its relevance.

In these multivariable models, continuous variables were modelled using restricted cubic smoothing splines with five knots to relax the assumption that continuous variables were linearly related to the mortality [ 33].

Firstly, the log modelled and environment variables are important for accurate NO2 prediction, which is evidenced by an increase in RMSPE for Models 4, 5 and 7 compared with Model 1.

In Model 2, categorical variables were modelled as fixed effects and continuous variables were modelled non parametrically by smooth function f j s.

To consider these uncertainties, the model uncertainty parameters, uncertain model input parameters and actual performance variables are modelled as random variables, and their distributions are updated simultaneously using Bayes' theorem.

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