Sentence examples for binomial regression was based from inspiring English sources
The use of Poisson regression over the negative binomial regression was based on the fact that the data is count variable and the majority of the poultry farmers in the data answered positively, but a few poultry farmers had zero response.
Our choice of log-binomial versus the more common logistic regression was based on the fact that odds ratios obtained from logistic regression tend to overestimate the true effect size when the outcome under study is common (>20%) and because prevalence ratios are more readily interpretable compared to odds ratios in cross-sectional studies [ 46, 47].
The selection of Poisson or negative binomial regression models was based on the model fit, assessed using a Pearson chi-square test.
Based on the likelihood ratio test, negative binomial regression was a better fit for the data than a Poisson regression.
Log binomial regression was used to calculate adjusted PRs.
A negative binomial regression was used due to over dispersion.
Negative binomial regression was used instead of Poisson regression to account for overdispersion in the data.
Negative binomial regression was used instead of Poisson regression because of the over-dispersed data.
Negative binomial regression would be chosen if the p-value of the Vuong test was not significant (p > 0.05) indicating the zero-inflated negative binomial regression was not significantly better than the negative binomial regression.
Binomial regression is considered the most adequate choice.
For all regression models, the decision to use a negative binomial model instead of a Poisson regression model was based on evaluating the Akaike Information Criterion (AIC) value of both models and the significance of the over-dispersion term of the negative binomial model.
