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We test the influence of an array of explanatory factors on driver's license possession, using a binomial logistic model, and on license timing, using multilevel survival analysis and censored regression models.
A multivariate analysis was performed to identify independent factors using a binomial logistic regression.
Using a binomial logistic regression analysis of the graduates' answers on the matching question, we estimate for each education programme whether there is a significant difference between the graduates' match within their occupational domain and outside it.
We modelled the relationship between GRPs and smoking prevalence using a binomial logistic generalized additive model.
We performed generalised linear mixed models (GLMMs) with SPSS 19 for the dependent variables "participation" (yes/no) and "performance" (correct/incorrect) using a binomial logistic regression.
Health workers' self-reported likelihood of leaving their jobs was modelled using a binomial logistic regression model, with the variable dichotomized into not at all or not very likely vs. fairly or very likely.
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We used a binomial logistic regression (logit link) to obtain parameter estimates for prediction of egg rejection probability.
To evaluate the association of CAC progression across baPWV quartiles, we used a binomial logistic regression model to estimate odds ratios (ORs) with 95%% confidence intervals (CIs) for CAC progression.
This association was further tested using a binomial multiple logistic regression analysis for which sex and age were included as covariates.
Mortality rates were investigated using a multilevel binomial logistic regression model, with 'Group' as a categorical predictor and random intercepts for practices.
The frequency of systemically infected plants was fitted to a Binomial logistic regression equation using GLM; "host", " pipo" and "dpi" (i.e., time at which inoculated leaves were removed) were treated as factorial random factors in the model.
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