Sentence examples for bivariate regression were selected from inspiring English sources

Variables with a p value <0.10 on bivariate regression were selected for the multivariable analysis.

Based on the outcome of this analysis, the predictor variables for use in multivariable logistic regressions were selected a priori, if they were significant at the 0.10 level in the bivariate analysis or if they were considered important.

Variables showing a p value of <0.2 after bivariate regression analysis were selected for the multivariate regression model.

All variables with P < 0.2 from bivariate analysis were selected for multivariate logistic regression.

Independent variables with p-values less than 0.3 in the bivariate analysis were selected for multivariate linear regression.

The variables that present p < 0.20 in the bivariate analysis were selected for multivariate analysis by logistic regression to establish a pain model prediction.

Factors with P-value 0.2 or less at bivariate analysis were selected for further multivariate analysis and entered into a logistic regression model.

Questions included in the regression model were selected based on whether they were statistically significant at the bivariate level.

Variables significantly associated with performance at the bivariate level plus available background characteristics were selected as independent variables in multiple regression analyses of performance (the dependent variable) – see Table 2. Analyses were done separately for prenatal and postpartum care performance.

Based on the results of bivariate analyses, clinically relevant and statistically significant independent variables were selected for multivariate linear regression analysis (Table 5; see Additional file Table 5).

* p <.005, **p < 0.01 Variables were selected as candidates for a simultaneous regression analysis based on the level of significance of the bivariate association with the dependent variable.