Sentence examples for binomial variability can be from inspiring English sources

12 In this way extra binomial variability can be accounted for in both the point estimate of the effect of treatment and the confidence intervals describing the degree of over dispersion in a manner adaptive to the observed clustering.

The within-study variability can be modeled using an approximate normal distribution [ 1, 2] or a binomial distribution [ 2, 14, 16].

When N is large enough, the binomial random variables can be approximated with a Gaussian distribution.

Unlike the binomial distribution, the beta binomial can be convex as well as concave.

That's a practical advantage over the calculus-based Black-Scholes-Merton model, because it means binomial models can be built using standard spreadsheet software.

In terms of the concept of independence, the experiment leading to the binomial distribution can be described as follows.

Binomial data can be processed more efficiently on MapReduce.

For instance, comparisons between Zero inflated count models with ordinary Poisson, or Zero inflated negative binomial against ordinary negative binomial model can be done using Voung test.

For smaller regions, the binomial probabilities can be calculated directly.

An HGT calculation takes O(N) time, assuming binomial coefficients can be calculated in constant time.

Again Poisson, negative binomial, zero-inflated Poisson or zero-inflated negative binomial regression can be used to model such count data depending on the extent of overdispersion.