Sentence examples for binomial count model was from inspiring English sources

Specifically, a negative binomial count model was estimated due to overdispersion of total injuries.

If a Poisson or negative binomial distribution based count model is applied to data with an excess of zeros without addressing these mixtures, the model can be strongly affected.

Among baris in the negative binomial count model, population is highly statistically significant and had the largest coefficient.

The negative binomial regression model was appropriate in analyzing non-negative integers count data such as DMFT score and the number of periodontally healthy sextants.

For each count data outcome, selection of a Poisson or negative binomial regression model was made by model fit, which was determined by the Pearson chi-square test.

A negative binomial regression model was used to estimate relative risks when analyzing the score on the CES-D scale as overdispersed count data.

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

A negative binomial model was used because the outcome was count data and the majority of participants had no repeat visits.

A binomial model was used for the probability of encountering terns, and a negative binomial was used for the number of terns if encountered.

The beta binomial model was used to account for over-dispersion.

The negative binomial regression model is appropriate for count data and is similar to the Poisson regression model except that it can work with over-dispersion in the data.