# AP Statistics Curriculum 2007 Gamma

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- | Note that if \( \{X_1,X_2,X_3,\cdots \}\) is a sequence of independent [[AP_Statistics_Curriculum_2007_Exponential|Exponential random variables]] then \(Y_k = \sum_{i=1}^k{X_i} \) is a [http://www.math.uah.edu/stat/special/Gamma.html random variable with gamma distribution with some shape parameter], k (positive integer) and scale parameter b. By the [[AP_Statistics_Curriculum_2007_Limits_CLT|central limit theorem]], if k is large, then gamma distribution can be approximated by the normal distribution with mean \(\mu=kb\) and variance \(\sigma =kb^2\). That is, the distribution of the variable | + | Note that if \( \{X_1,X_2,X_3,\cdots \}\) is a sequence of independent [[AP_Statistics_Curriculum_2007_Exponential|Exponential random variables]] then \(Y_k = \sum_{i=1}^k{X_i} \) is a [http://www.math.uah.edu/stat/special/Gamma.html random variable with gamma distribution with some shape parameter], k (positive integer) and scale parameter b. By the [[AP_Statistics_Curriculum_2007_Limits_CLT|central limit theorem]], if k is large, then gamma distribution can be approximated by the normal distribution with mean \(\mu=kb\) and variance \(\sigma =kb^2\). That is, the distribution of the variable Z_k={{Y_k-kb}\over{\sqrt{k}b}}\) tends to the standard normal distribution as <math>k\longrightarrow \infty</math>. |

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## Revision as of 16:57, 23 June 2012

## Contents |

## General Advance-Placement (AP) Statistics Curriculum - Gamma Distribution

### 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 λ is

For , where *k* = *h* and θ = 1 / λ, the gamma probability density function is given by

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 Γ(
*k*) = (*k*− 1)! is the gamma function - θ = 1 / λ is the mean number of events per time unit, where λ 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 θ=1/2=0.5. If we want to find the mean number of calls in 5 hours, it would be 5 1/2=2.5.
- x is a random variable

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

where

- if k is a positive integer, then Γ(
*k*) = (*k*− 1)! is the gamma function

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

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

**Variance**: The gamma variance is

### 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 θ = 1 / 0.5 = 2 fish every hour on average. Using θ = 2 and *k* = 4, we can compute this as follows:

The figure below shows this result using SOCR distributions

### Normal Approximation to Gamma distribution

Note that if \( \{X_1,X_2,X_3,\cdots \}\) is a sequence of independent Exponential random variables then \(Y_k = \sum_{i=1}^k{X_i} \) is a random variable with gamma distribution with some shape parameter, k (positive integer) and scale parameter b. By the central limit theorem, if k is large, then gamma distribution can be approximated by the normal distribution with mean \(\mu=kb\) and variance \(\sigma =kb^2\). That is, the distribution of the variable Z_k={{Y_k-kb}\over{\sqrt{k}b}}\) tends to the standard normal distribution as .

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

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