negative binomial distribution


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negative binomial distribution

[¦neg·əd·iv bī¦nō·mē·əl ‚di·strə′byü·shən]
(statistics)
The distribution of a negative binomial random variable. Also known as Pascal distribution.
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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.
As the negative binomial distribution model has exponential conditional mean, its coefficient estimate can be interpreted as a semi-elasticity (Cameron and Trivedi 2009).
When fitting a negative binomial distribution to the entire pond using a Chi-Square test, the distribution was not statistically different from a negative binomial distribution at P = 0.
This finding was analogous to the work of Pollard et al (1977) when fitting the negative binomial distribution to groups of players.
lf you were to believe that the stable distribution or the negative binomial distribution were the only two hypotheses to be considered, considered them equally likely (and were willing to overlook the negative and fractional home run predictions of the stable distribution) the "weight of the evidence" (Good 1981; Peirce 1878) would still be against the power law distribution.
is the probability function of a negative binomial distribution with parameters equal to
A negative binomial distribution best described the census data ([chi square] = 3.
Consequently the reported results correspond to the negative binomial distribution model.
For all days, trap distributions were not significantly different from the negative binomial distribution (p>0.
A negative binomial distribution will converge to a Poisson as the variance approaches the mean (Bliss and Fisher, 1953).
The negative binomial distribution has been used for about 50 years to model the distribution of certain biological data.