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Exact(30)
A Bernoulli (binomial) distribution was used for presence/absence models and a Poisson distribution for count models.
Negative binomial distribution was used for curve fitting.
A binomial distribution was used.
The negative binomial distribution was used in the GLM models as the Poisson assumption was violated.
The binomial distribution was used to compute exact confidence intervals for proportions.
The binomial distribution was used to compute the significance level of the McNemar test.
Similar(30)
The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k.
For both the distance model and spatially-explicit model, deviance goodness-of-fit tests and overdispersion parameters (values ≠1 conflict with the assumption of binomial distribution) were used to determine whether the sample data followed a binomial distribution, and corrections for overdispersion were applied where needed [31].
Finally, a negative binomial distribution is used to fit the counts for each gene.
Generalized linear models with negative binomial distribution were used to analyze the effect of comorbidities on health care costs.
Chi square tests (comparison to the binomial distribution) were used to analyse the Y-tube olfactometer choice test data.
More suggestions(15)
binomial regression was used
binomial distribution was assumed
binomial probability was used
binomial distribution was adopted
binomial distribution were used
binomial distribution was determined
binomial distribution was established
binomial distribution was introduced
binomial distribution was selected
binomial distribution was performed
binomial distribution was found
binomial distribution was calculated
binomial distribution was proposed
binomial theorem was used
binomial distribution was preferred
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