Sentence examples for models and backward from inspiring English sources

To be more inclusive, the predictor variables and the treatment interactions with a P value ≤ 0.2 were selected as candidates in the multiple logistic regression models, and backward elimination was performed with a cutoff P value of < 0.07.

The predictor variables and the treatment interactions with P value ≤0.2 were selected as candidates in the multiple logistic regression models, and backward elimination was performed with a cutoff P value of <0.07.

Variables with a p value of <0.20 were included in the candidate models and backward selection was used to develop the two final multivariable models on factors associated with high knowledge and confidence.

As a further extension of this article, we can discuss possible applications based on the presented approach, as is the very famous Leondief model, see [4], or the very important Leslie population growth model and backward population projection, see also [4], the Host-parasitoid Models in physics, see [38] or the distribution of heat through a long rod or bar as suggested in [24].

Univariate predictors with a two-sided p-value ≤0.2 were included in the multivariate model, and backward elimination was applied to obtain a final model; two-sided p-values ≤0.05 were considered significant.

By implementing the SBL model and backward elimination (BE) procedure, we obtained the candidate CNA regions.

Variables with p < 0.10 in univariate analysis were introduced in the model, and backward analysis was performed.

Those shown to be associated will be entered into the multivariate logistic regression model and backward regression will be performed.

For the copy number data in the first dataset, we used a sparse Bayesian learning (SBL) model and backward elimination (BE) procedure to find candidate CNA regions.

Significant covariates (P ≤ 0.05) were then combined into a single model, and backward variable selection was performed to serially remove nonsignificant predictors (P ≥ 0.05).

At the start, all variables were included in the model and backward elimination process was employed to remove non-significant variables.