Poisson distribution: Difference between revisions

From ACT Wiki
Jump to navigationJump to search
imported>Administrator
(CSV import)
 
imported>Doug Williamson
m (Categorise.)
 
(10 intermediate revisions by 2 users not shown)
Line 1: Line 1:
A discrete probability distribution that gives the expected frequency of occurrence of certain types of random events.
<i>Statistics</i>.
 
A probability model used where discrete events occur in an apparently random manner, subject to an observable average rate.
 
This rate parameter is the only parameter required to specify fully the probability distribution function of a Poisson random variable.
 
For example, the number of business interruptions occurring in a given time period, or the number of admissions to a hospital A & E department in a given time period.
 
 
The Poisson distribution can be an appropriate model for processes where:
#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 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]]
* [[Discrete random variable]]
* [[Binomial distribution]]
* [[Frequency distribution]]
* [[Frequency distribution]]
* [[Probability]]
* [[Probability]]


[[Category:The_business_context]]

Latest revision as of 14:02, 21 March 2018

Statistics.

A probability model used where discrete events occur in an apparently random manner, subject to an observable average rate.

This rate parameter is the only parameter required to specify fully the probability distribution function of a Poisson random variable.

For example, the number of business interruptions occurring in a given time period, or the number of admissions to a hospital A & E department in a given time period.


The Poisson distribution can be an appropriate 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