Par yield and Poisson distribution: Difference between pages
From ACT Wiki
(Difference between pages)
imported>Doug Williamson (Colour change of example headers) |
imported>Doug Williamson (Add 'with no upper limit' to differentiate from binomial distribution.) |
||
Line 1: | Line 1: | ||
<i>Statistics</i>. | |||
A probability model used where discrete events occur in a continuum. | |||
For example, the number of phone calls received in a given time period. | |||
The Poisson distribution can be a useful model for processes where: | |||
#Continuous observation is needed, rather than a number of independent trials. | |||
#The random variable takes a positive whole number (integer) value, with no upper limit. | |||
#The expected number of occurrences is known or can be estimated, and | |||
#Primary interest is in the number of times an event occurs within a particular period. | |||
== See also == | == See also == | ||
* [[ | * [[Discrete random variable]] | ||
* [[ | * [[Binomial distribution]] | ||
* [[ | * [[Frequency distribution]] | ||
* [[ | * [[Probability]] | ||
Revision as of 11:06, 7 August 2014
Statistics.
A probability model used where discrete events occur in a continuum.
For example, the number of phone calls received in a given time period.
The Poisson distribution can be a useful model for processes where:
- Continuous observation is needed, rather than a number of independent trials.
- The random variable takes a positive whole number (integer) value, with no upper limit.
- The expected number of occurrences is known or can be estimated, and
- Primary interest is in the number of times an event occurs within a particular period.