Sentence examples for bivariate analysis to identify from inspiring English sources

Results will be compared with the bivariate analysis to identify factors that lose significance or strength to others.

We employed bivariate analysis to identify any significant differences between the two groups with respect to socio-economic variables.

Adewuya et al. [ 47] conducted a stepwise regression analysis followed by bivariate analysis to identify predictors of PTSD in Nigerian women at 6 weeks postpartum.

We used univariate and bivariate analyses to describe the magnitude and pattern of abuse in the sample and bivariate analysis to identify risk factors for abuse.

Bivariate logistic regression analysis was used for the calculation of the odds ratios (OR) with 95 % Confidence Interval (CI) and P values in bivariate analysis to identify association between patient's characteristic and 30-day mortality.

Bivariate analysis to identify associations between oral hygiene habits (i.e. the dependent variables namely frequency of tooth brushing, tool used and duration of toothbrushing) and sex, age, geo-political zone, and educational status (the independent variables) of the study participants was also carried out.

This outlier was not included in bivariate and multivariate analysis to identify predictors of hair mercury concentrations.

Binary logistic regression analysis was used for the calculation of the odds ratios (OR) and p values in bivariate and multivariate analysis to identify association between patients' characteristic and 30-day mortality.

Multivariate logistic regression analysis to identify predictors of poor environments (scores ≥ 3 versus ≤ 2) showed similar findings to those summarized in Table 3 for bivariate comparisons.

All variables that were identified by bivariate analysis to have statistical significance in predicting glucose screening were included in a single logistic regression model.

Because the bivariate effects of the predictors on the dependent variable were probably confounded by other factors, multiple liner regression analysis was further used to examine the predicting effect of each potential predictor identified in the bivariate analysis to adjust for the effects of other confounding variables.