Poisson distribution: Difference between revisions
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imported>Doug Williamson m (Add 'integer' to 3.) |
imported>Doug Williamson (Add 'with no upper limit' to differentiate from binomial distribution.) |
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The Poisson distribution can be a useful model for processes where: | The Poisson distribution can be a useful model for processes where: | ||
#Continuous observation is needed, rather than a number of independent trials. | #Continuous observation is needed, rather than a number of independent trials. | ||
#The random variable takes a positive whole number (integer) value. | #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 | #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. | #Primary interest is in the number of times an event occurs within a particular period. | ||
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.
