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The Negative Binomial distribution estimates the total number of trials there will be before s successes are achieved where there is a probability p of success with each trial. The total number of trials is equal to the number of failures plus the s successes. Examples of the Negative Binomial distribution are shown below: #### Uses

##### Binomial examples

The NegBinomial distribution has two applications for a binomial process:

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For example, a hospital has received a total of 17 people with a rare disease in the last month. The disease has a long incubation period. There have been no new admissions for this disease for a fair number of days. The hospital knows that people infected with this problem have a 65% chance of showing symptoms. It is also known that all people with symptoms will turn up at the hospital. How many other infected people are there out in the community? The answer is NegBinomial(65%,17+1)-(17+1). IF we knew (we don't) that the last person to be infected was symptomatic, the answer would be NegBinomial(65%,17) - 17. The total number infected would be NegBinomial(65%,17+1) -1.

##### Poisson example

The Negative Binomial distribution is frequently used in accident statistics and other Poisson processes because the Negative Binomial distribution can be derived as a Poisson random variable whose rate parameter lambda is itself random and Gamma distributed, i.e.:

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The Negative Binomial distribution therefore also has applications in the insurance industry, where for example the rate at which people have accidents is affected by a random variable like the weather, or in marketing. This has a number of implications: it means that the Negative Binomial distribution must have a greater spread than a Poisson distribution with the same mean; and it means that if one attempts to fit frequencies of random events to a Poisson distribution but find the Poisson distribution too narrow, then a Negative Binomial can be tried and if that fits well, this suggests that the Poisson rate is not constant but random, and can be approximated by the corresponding Gamma distribution (see here ).