Sentence examples for bivariate analyses were considered from inspiring English sources

Associations yielding a p value of less than 0.10 in bivariate analyses were considered for the multiple regression model.

All clinically plausible variables with P < 0.20 in the respective bivariate analyses were considered for the models.

Predictors found to be significant at p ≤ 0.2 in the bivariate analyses were considered for inclusion in the hierarchical multivariate modeling.

For the multivariate regression model, only those variables that were significantly associated (p < 0.05) with the outcome risk behavior in bivariate analyses were considered.

*Statistically significant at (p < 0.05), OR, Crudes Odds Ratio aSubstances were khat, Cigarettes, Shisha and hashis In order to identify independent predictors, variables that showed statistical significance in bivariate analyses were considered for multiple logistic regressions.

Covariates that were significant predictors (p < 0.05) of each of the three outcomes of interest from the bivariate analyses were considered in models of NO2 and atopy for each time window.

A logistic regression analysis was used to identify the best predictors of pneumococcal vaccination status from those variables which had a statistically significant association in the above bivariate analyses or were considered by the investigators to be theoretically significant.

Bivariate analyses were explored using Chi-2 tests and a p-value of p < 0.05 was considered statistically significant.

Bivariate analyses were performed using the Fisher exact test.

Bivariate analyses were conducted to identify significant associations among variables.

Bivariate analyses were used to examine the differences between women with and without VWD.