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Results were combined on a bivariate plot to identify health posts at the high and low extremes of efficiency, which should be followed up to determine how and why their production processes are operating differently.
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We conducted bivariate analyses to identify any demographic or practice characteristics associated with comfort levels.
We used bivariate analyses to identify variables potentially predicting the presentation of MI.
Model-building involved consideration of variables, using a series of bivariate analyses to identify potential confounders.
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.
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.
Funnel plot failed to identify publication bias.
The bivariate analyses undertaken to identify confounding variables are presented in Table 2.
Weighted Pearson partial correlations were used for bivariate comparisons and to identify predictors for the multiple regression models.
Bivariate analyses were conducted to identify significant associations among variables.
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