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

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'''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. | '''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. | ||

− | <br />'''Probability density function''': The waiting time until the hth Poisson event with a rate of change <math>\lambda</math> is | + | <br />'''Probability density function''': The waiting time until the hth Poisson event with a rate of change <font size="3"><math>\lambda</math></font> is |

:<math>P(x)=\frac{\lambda(\lambda x)^{h-1}}{(h-1)!}{e^{-\lambda x}}</math> | :<math>P(x)=\frac{\lambda(\lambda x)^{h-1}}{(h-1)!}{e^{-\lambda x}}</math> | ||

− | For X | + | For <math>X\sim Gamma(k,\theta)</math>, where <font size="3"><math>k=h</math></font> and <font size="3"><math>\theta=1/\lambda</math></font>, the gamma probability density function is given by |

:<math>\frac{x^{k-1}e^{-x/\theta}}{\Gamma(k)\theta^k}</math> | :<math>\frac{x^{k-1}e^{-x/\theta}}{\Gamma(k)\theta^k}</math> | ||

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*k is the number of occurrences of an event | *k is the number of occurrences of an event | ||

*if k is a positive integer, then <font size="3"><math>\Gamma(k)=(k-1)!</math></font> is the gamma function | *if k is a positive integer, then <font size="3"><math>\Gamma(k)=(k-1)!</math></font> is the gamma function | ||

− | *<math>\theta=1/\lambda</math> is the mean number of events per time unit, where <math>\lambda</math> 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 <math>\theta</math>=1/2=0.5. If we want to find the mean number of calls in 5 hours, it would be 5 <math>\times</math> 1/2=2.5. | + | *<font size="3"><math>\theta=1/\lambda</math></font> is the mean number of events per time unit, where <font size="3"><math>\lambda</math></font> 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 <font size="3"><math>\theta</math>=1/2=0.5</font>. If we want to find the mean number of calls in 5 hours, it would be <font size="3">5 <math>\times</math> 1/2=2.5</font>. |

*x is a random variable | *x is a random variable | ||

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where | where | ||

− | *if k is a positive integer, then <math>\Gamma(k)=(k-1)!</math> is the gamma function | + | *if k is a positive integer, then <font size="3"><math>\Gamma(k)=(k-1)!</math></font> is the gamma function |

*<math>\gamma(k,x/\theta)=\int_0^{x/\theta}t^{k-1}e^{-t}dt</math> | *<math>\gamma(k,x/\theta)=\int_0^{x/\theta}t^{k-1}e^{-t}dt</math> | ||

## Revision as of 15:46, 11 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 \(X\sim Gamma(k,\theta)\), 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
- \(\gamma(k,x/\theta)=\int_0^{x/\theta}t^{k-1}e^{-t}dt\)

**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