Sentence examples for model variables with from inspiring English sources

From the solution of the optimization of AGVs (Autonomous Guided Vehicles) Network [1] resulted techniques of implementation of logical functions over variables of the model only with algebraic expressions and the division of two model variables with the aid of auxiliary binary variables.

In both models adjusted odds ratios (AORs), 95% confidence intervals (95%CI) and a test for the overall significance of the model variables with more than 2 levels (Type 3 analysis of effects test) were computed to assess pairwise comparisons.

Each health profile described unique facets of physical and mental health (internal GoM model variables) plus differences in socio-demographic characteristics (external GoM model variables) with a clear economic gradient (lower education and employment sector) when moving from high to low economic categories within each profile.

In this model, variables with P-value <0.1 were entered.

For the final multivariable model variables with p-value > 0.05 were excluded with backward elimination procedure [ 21].

This test was initially performed by including in the model variables with a univariate likelihood ratio p value < 0.20.

Initial variables were selected by developing univariate models; variables with Wald p>0.2 were excluded from further analysis.

However, modelling variables with a large number of categories introduces more complexity and uncertainties in estimating parameters.

Initially, associations between potential covariates were assessed by univariate log-binomial regression models; variables with p < 0.2 were included in the full model.

In further models, variables with a P < 0.10 in univariate linear regression analyses for associations with the CER were selected for multivariate analysis.

Logistic regression was used to model variables associated with undetectable RNA PCR viral load.