# Difference between revisions of "AP Statistics Curriculum 2007 Gamma"

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The figure below shows this result using [http://socr.ucla.edu/htmls/dist/Gamma_Distribution.html SOCR distributions] | The figure below shows this result using [http://socr.ucla.edu/htmls/dist/Gamma_Distribution.html SOCR distributions] | ||

<center>[[Image:Gamma.jpg|600px]]</center> | <center>[[Image:Gamma.jpg|600px]]</center> | ||

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+ | * SOCR Home page: http://www.socr.ucla.edu | ||

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## Revision as of 17:36, 18 July 2011

### Gamma Distribution

**Definition**: Gamma distribution is a distribution that arises naturally in processes for which the waiting times between events are relevant. It can be thought of as a waiting time between Poisson distributed events.

**Probability density function**: The waiting time until the hth Poisson event with a rate of change \(\lambda\) is

\[P(x)=\frac{\lambda(\lambda x)^{h-1}}{(h-1)!}{e^{-\lambda x}}\]

For \(X\sim \operatorname{Gamma}(k,\theta)\!\), where \(k=h\) and \(\theta=1/\lambda\), the gamma probability density function is given by

\[\frac{x^{k-1}e^{-x/\theta}}{\Gamma(k)\theta^k}\]

where

- e is the natural number (e = 2.71828…)
- k is the number of occurrences of an event
- if k is a positive integer, then \(\Gamma(k)=(k-1)!\) is the gamma function
- \(\theta=1/\lambda\) is the mean number of events per time unit, where \(\lambda\) is the mean time between events. For example, if the mean time between phone calls is 2 hours, then you would use a gamma distribution with \(\theta\)=1/2=0.5. If we want to find the mean number of calls in 5 hours, it would be 5 \(\times\) 1/2=2.5.
- x is a random variable

**Cumulative density function**: The gamma cumulative distribution function is given by

\[\frac{\gamma(k,x/\theta)}{\Gamma(k)}\]

where

- if k is a positive integer, then \(\Gamma(k)=(k-1)!\) is the gamma function
- \(\textstyle\gamma(k,x/\theta)=\int_0^{x/\theta}t^{k-1}e^{-t}dt\)

**Moment generating function**: The gamma moment-generating function is

\[M(t)=(1-\theta t)^{-k}\!\]

**Expectation**: The expected value of a gamma distributed random variable x is

\[E(X)=k\theta\!\]

**Variance**: The gamma variance is

\[Var(X)=k\theta^2\!\]

### Applications

The gamma distribution can be used a range of disciplines including queuing models, climatology, and financial services. Examples of events that may be modeled by gamma distribution include:

- The amount of rainfall accumulated in a reservoir
- The size of loan defaults or aggregate insurance claims
- The flow of items through manufacturing and distribution processes
- The load on web servers
- The many and varied forms of telecom exchange

The gamma distribution is also used to model errors in a multi-level Poisson regression model because the combination of a Poisson distribution and a gamma distribution is a negative binomial distribution.

### Example

Suppose you are fishing and you expect to get a fish once every 1/2 hour. Compute the probability that you will have to wait between 2 to 4 hours before you catch 4 fish.

One fish every 1/2 hour means we would expect to get \(\theta=1 / 0.5=2\) fish every hour on average. Using \(\theta=2\) and \(k=4\), we can compute this as follows:

\[P(2\le X\le 4)=\sum_{x=2}^4\frac{x^{4-1}e^{-x/2}}{\Gamma(4)2^4}=0.12388\]

The figure below shows this result using SOCR distributions

- SOCR Home page: http://www.socr.ucla.edu

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