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The choice of a binomial distribution model was adequate to assess overdispersion of A-type particles (Rosner, 2011 ).
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DESeq implements a model based on negative binomial distribution; this model was developed with special attention to coping with biological variance and was run under R release 2.15.2.
Distribution of mutations among CDR and FR gene segments was evaluated by the Chang Casali binomial distribution model [46].
The P-value was calculated based on a negative binomial distribution model, and adjusted by Benjamini-Hochberg mothed.
A binomial distribution family was modelled using the logit function.
The binomial distribution is modelled by a logit link function, while the Poisson distribution is modelled by a log link function.
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.
Data followed a binomial distribution which was accounted for in the model.
Costs were log-transformed due to the skewed distribution of medical expenditures, and the negative binomial distribution was used to model LOS to account for overdispersion.
A negative binomial distribution was adopted to model the data due to the presence of extra-Poisson variation in the case counts.
In the generalized linear regression model, the negative binomial distribution was used and the log number of patient-days was treated as an offset parameter.
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