Poisson distribution: Difference between revisions

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imported>Doug Williamson
(Amend illustration from 'phone calls received' to 'business interruptions occurring'.)
imported>Doug Williamson
(Add 'finite' before 'number' for clarity.)
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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 finite number of independent trials.
#The random variable takes a positive whole number (integer) value, with no upper limit.
#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

Revision as of 11:11, 7 August 2014

Statistics.

A probability model used where discrete events occur in a continuum.

For example, the number of business interruptions occurring in a given time period.


The Poisson distribution can be a useful model for processes where:

  1. Continuous observation is needed, rather than a finite number of independent trials.
  2. The random variable takes a positive whole number (integer) value, with no upper limit.
  3. The expected number of occurrences is known or can be estimated, and
  4. Primary interest is in the number of times an event occurs within a particular period.


See also