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# Normal approximation to negative binomial distribution-1

Course Topic(s): Probability | Continuous Distributions, normal | Discrete Distributions, geometric and negative binomial | Convergence Theorems, central limit theorem

Normal approximation to the Negative Binomial is valid when the number of required successes, $$s$$, is large, and the probability of success, $$p$$, is neither very small nor very large. This approximation can be justified via Central Limit Theorem, because the NegBin($$s$$, $$p$$) distribution can be thought of as the sum of $$s$$ independent NegBin(1, $$p$$) distributions. In practice, some difficulty is knowing whether the values for $$s$$ and $$p$$ fall within the bounds for which the Normal distribution is a good approximation. The smaller the value of $$p$$, the longer the tail of a NegBin(1,$$p$$) distribution would be.

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Subject classification(s): Univariate Distributions | Probability | Statistics and Probability

Creator(s): Vose Software

Contributor(s): Vose Software BVBA, Iepenstraat 98, Gent 9000, Belgium

This resource was cataloged by Ivo Dinov

Publisher:
Vose Software

This review was published on September 19, 2012