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The required probability must be calculated using the binomial probability formula.
It is best to create a detailed binomial decision tree to ensure that all potential outcomes have been incorporated into the analysis.
Linnaean binomials may be descriptive or geographical.
The results are produced using a negative binomial model, with standard errors adjusted for clustering on individual senators.
There have been few attempts at modelling parasite abundance data using the negative binomial distribution to describe the error variance.
Confidence limits (lines) are based on the negative binomial distribution with k l 4n96.
The negative binomial distribution is widely used for empirical description of the dispersion of parasites among hosts.
Each of the simulated distributions was overdispersed and well fitted by a negative binomial distribution.
However, the negative binomial distribution does not allow ready extension to additional levels of nested random variation in the data.
The process of recovery and counting of larvae is described by a model based on the binomial distribution.
Neither elementary acquisition nor adolescent acquisition were significant in the binomial regression analysis conducted on the group of speakers as a whole.
A success rate beyond chance would be above .67 by the binomial theorem.
Fitted by the second method, the negative binomial distribution has its average burstiness matching exactly that of the data it is fitted to.
First, for e2e2t < 1, the terms involving 1 + e2(e2t- 1) can be expanded in binomial series.
Confidence limits for proportions were obtained from the binomial distribution.
The probability calculations used in this application are based on standard lot acceptance sampling techniques using the binomial distribution.
The binomial type of germination data required transformation before using procedures for mean comparisons or regression to achieve linearity.
