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 ?
The k parameter of the negative binomial distribution estimated by the maximum likelihood method is calculated iteratively and is the value that equates the two members of the following (Bliss and Fisher, 1953):
t] can then actually be obtained by using the relationship between the cumulative beta distribution and the cumulative binomial distribution function as follows (Daly [13] and Johnson et al.
We fit the transmission data from patients within subgroups to the negative binomial distribution with mean R and dispersion parameter k, which characterizes individual variation in transmission, including the likelihood of superspreading events (i.
The parameters a and b of the beta-binomial model can be chosen to provide flexibility to handle many possible situations in health services research that have this "probability" nature of constraining between 0 and 1, and are more diffuse than the over-dispersion capabilities of the negative binomial distribution (Morris and Lock 2009).
The simulations were carried out by fitting unique negative binomial distributions to each player pairing in a side
It seems intuitive to model the accident process by some classic count distribution such as the Poisson distribution because its interpretation is direct, as a limit of a Binomial distribution with the number of tries going to infinity and the accident probability tending to 0.
Goodness-of-fit statistics for the lognormal, Poisson, and negative binomial distributions were calculated for the four individual species and for all species to help judge which error structure best characterized the MRFSS catch-rate data.
Given the connection between geometric and negative binomial distributions, applications of these stochastic models hinge on characteristics of a specific setting.
When evaluated against results of the computer simulations, neither the Poisson nor binomial distributions of female mating success could be rejected ([[Chi].
These are the conventional constraints for N and n in both the Poisson and the binomial distributions.
In terms of sampling actual populations, the normal distribution implies that most sampling units contain at least one individual of the species or group of interest, whereas the Poisson or negative binomial distributions do not.
We do this for the normal and binomial distributions.