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We conducted a bivariate analysis to study the relation between GHD and the dichotomized variables and calculate odds ratios.
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We also used bivariate analysis to examine associations with socioeconomic indicators (Table 3).
Using statistical software 'R' V.3.0.3, 33 we performed bivariate Spearman's rank correlation analysis to study the association between the calculated private sector Hib vaccine coverage (state-wide) and those state-specific socioeconomic factors that influence vaccination coverage rates.
We used bivariate analysis to estimate odds ratios (ORs) with 95% confidence intervals (CIs).
We used analysis of variance for bivariate analysis to explore associations and potential confounders.
After bivariate analysis to test the hypotheses using Pearson correlation, we performed multiple regression analysis.
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.
Logistic regression analysis (bivariate) was used to study the risk factors associated with kala-azar infections.
The relation between post-procedure headaches and the risk factors found significant in bivariate analysis was studied with binary logistic regression; the best fit chosen based on likehood ratio.
The significant factors derived from the bivariate analysis were studied separately using unadjusted relative risk (95% confidence interval).
A first descriptive analysis of the various parameters collected was carried out A bivariate analysis was then performed to study the factors affecting morbidity and mortality in digestive surgery The comparison was made using the student's t test for quantitative variables and the Chi square for the qualitative variables.
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