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Associations were investigated by bivariate and multiple linear regression analyses.
The strength of the association of potential predictor variables for the occurrence of tuberculosis was analyzed by bivariate and multiple stepwise conditional logistic regressions.
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We conducted bivariate and multiple regression analyses to test for differences in several variables of interest between controls and cases.
We used SAS version 9.2 for the univariate, bivariate, and multiple linear regression analyses and GeoDa version 0.9.8.14 for the spatial regression analyses.
Bivariate and multiple logistic regression (MLR) analysis.
Bivariate and multiple linear regressions were performed.
Descriptive statistics, bivariate and multiple regression analyses were performed.
Bivariate and multiple logistic regression models were fitted.
Descriptive analyses, exploratory factor analyses for scale development, bivariate correlation analyses and bivariate and multiple linear regression analyses were conducted.
Analyses of variance, and linear bivariate and multiple regression analysis were also performed.
Predictors for LBW were determined using bivariate and multiple logistic regression models.
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