Sentence examples for using a binomial regression from inspiring English sources

The results for each country were compared with the percentages for all countries combined by using a binomial regression model.

Analysis was carried out at the practice level using a binomial regression based on the calculated numerators and the available denominators.

The slopes and intercepts for the risk levels were generated using a binomial regression model with an identity link function as described previously [ 35].

Data were analysed using a generalised linear model using a binomial regression; odds ratios (and 95% confidence intervals) were reported as measures of association.

The dichotomous variable, with categories defined as either 'guideline-consistent' or 'guidelineinconsistent', will be analysed using a binomial regression model (primary analysis).

For women who chose a specific method (e.g. pill), the percentage of women who selected this method for a particular reason was compared for each country with the percentage for all countries combined by using a binomial regression model.

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.

In any case, it is also worth mentioning that in the work of Braña and Antón (2011) for Spain, the results obtained using the PCA component as dependent variables are similar to the one obtained using a binomial negative regression where the number of items was the outcome of interest.

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.