binomial distribution

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Related to binomial distributions: Normal distributions, Poisson distributions

binomial distribution

[bī′nō·mē·əl ‚dis·trə′byü·shən]
(statistics)
The distribution of a binomial random variable; the distribution (n,p) is given by P (B = r) = (nr) prqn-r, p + q = 1. Also known as Bernoulli distribution.
References in periodicals archive ?
Parameters R and k of the negative binomial distribution are the reproductive number and dispersion parameter, respectively.
This finding was analogous to the work of Pollard et al (1977) when fitting the negative binomial distribution to groups of players.
1973) Collegiate football scores and the negative binomial distribution.
is the probability function of a negative binomial distribution with parameters equal to
For insureds who reported at least once, the predictive distribution returns to a standard multivariate negative binomial distribution because the first parts of the numerator and the denominator of Equation (31) fall.
To isolate the consequences of possible model misspecification in deriving standardized indices of abundance, negative binomial distributions with characteristics like those of MRFSS recreational catch-per-trip distributions were simulated by using the SAS RANTBL function (SAS, 2000).
However, the calculated test statistics for the negative binomial distributions were at least an order of magnitude smaller than those for the Poisson and lognormal distributions, suggesting that an underlying negative binomial distribution was much more likely.
2]) by using the equation for the jth moment of a binomial distribution
The associated binomial distribution statistics indicate when student responses were at non-random levels that would be expected if students were not simply guessing.
n](q) with q [greater than or equal to] 2 has power against the violation of the overidentifying restriction for any departures from the Poisson distribution for either the binomial or negative binomial distribution.
They therefore use the overdispersed negative binomial distribution and anticipate that the negative binomial provides a much better fit in the tail region (see Table 1), an observation confirmed by the Pearson [chi square] test using the parameters based on MLE.
Each plot shows the observed and the predicted proportions estimated by the binomial distribution of the delta lognormal model.