Sentence examples for binomial regression was chosen from inspiring English sources

Negative binomial regression was chosen for the multivariate analyses due to two important characteristics of the dependent variable: (a) data were actual counts of the number of IUU fishing vessel visits to the countries, and (b) data were over-dispersed.

Negative binomial regression was chosen because some count data were overdispersed (Kim and Kriebel, 2009).

For the number of sextants with healthy gums, negative binomial regression was chosen as well (Vuong test: z = 0.65, p = 0.257).

Log linear binomial regression was chosen because when an outcome variable is common (>5%), logistic regression tends to overestimate the association between the independent variable of interest and the outcome.

A binomial logistical regression was chosen since the response was binary (i.e. "SRE expressed/No SRE expressed"), and can be understood as a series of Bernoulli trials with the log of the odds ratio as the linking function.

The negative binomial regression model was chosen to relax the assumption of mean and variance equality in the Poisson distribution of counts 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.

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.