binomial distribution


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Related to binomial distribution: Poisson distribution, normal distribution

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.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Abundances of the small size nemertean species fit the negative binomial distribution. The value of negative-binomial exponent (k) of 1.22 indicated an aggregated pattern but somewhat more dispersed than that of S.
Sixth, one data set (Mixon) was satisfactorily fit by two distributions: the negative binomial distribution and the inflated geometric distribution.
where k is the k exponent of negative binomial distribution, m is the mean, and P(x) is the probability of x individuals in a sampling unit.
Neutrosophic Binomial Distribution: The neutrosophic binomial random variable 'x' is then defined as the number of success when we perform the experiment n [greater than or equal to] 1 times.
In the MT model, a pure binomial distribution cannot be assumed because the total number of states is greater than 2; however, a similar variance structure can be achieved by treating each of the transitions as a separate binomial distribution and summing the variances together.
This example shows that the binomial distribution can arise in the context of life tables constructed from Australian data.
The values of [[??].sup.L.sub.t] and [[??].sup.U.sub.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.
Fact 2 Let X have a binomial distribution. For every R [greater than or equal to] 6E[X] we have
The researchers used generalized linear model (GLM) for modelling the frequency of women's visits for ANC, where response followed the negative binomial distribution. For final selection two criterions, including AIC and BIC were used.
We next fit the negative binomial distribution to the transmission data and to various data subsets according to patients' circumstances.
It presents a systems view of prognostic and sustainability-based maintenance management, then addresses data processing and probability distribution functions like uniform, geometric, normal, and binomial distribution; signal processing and its application to mechanical condition monitoring and fault identification; health monitoring and prognosis, the degradation process, the main parts of a real-time monitoring system, and fault prognosis and the methods for remaining useful life prediction; prediction methods in machine prognosis; maintenance planning and scheduling techniques, including scheduling modeling and grouping technology-based maintenance; and prognosis incorporating maintenance decision making and its role in the development of maintenance management.
The main contention of this paper relies on detecting the existence of skill with the generalized binomial distribution, using an established analogy with winning in sports.