Poisson distribution - Revision history

Doug: Add link.

2024-12-16T23:01:54Z

Add link.

← Older revision		Revision as of 23:01, 16 December 2024
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	* [[Binomial distribution]]		* [[Binomial distribution]]
	* [[Frequency distribution]]		* [[Frequency distribution]]
			* [[Parameter]]
	* [[Probability]]		* [[Probability]]

	[[Category:The_business_context]]		[[Category:The_business_context]]

imported>Doug Williamson: Categorise.

2018-03-21T14:02:31Z

Categorise.

← Older revision		Revision as of 14:02, 21 March 2018
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	* [[Frequency distribution]]		* [[Frequency distribution]]
	* [[Probability]]		* [[Probability]]

			[[Category:The_business_context]]

imported>Doug Williamson: Correct typo "fully", remove reference to future intended edits, add comma.

2014-09-04T14:03:13Z

Correct typo "fully", remove reference to future intended edits, add comma.

← Older revision		Revision as of 14:03, 4 September 2014
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	A probability model used where discrete events occur in an apparently random manner, subject to an observable average rate.		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 ~~full~~ the probability distribution function of a Poisson random variable.		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~~. We intend to return to the latter example in a future editing session~~.		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.

imported>Doug Williamson: Spacing.

2014-08-28T11:45:24Z

Spacing.

← Older revision		Revision as of 11:45, 28 August 2014
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	A probability model used where discrete events occur in an apparently random manner, subject to an observable average rate.		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 full the probability distribution function of a Poisson random variable.		This rate parameter is the only parameter required to specify full 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. We intend to return to the latter example in a future editing session.		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. We intend to return to the latter example in a future editing session.


	The Poisson distribution can be an appropriate model for processes where:		The Poisson distribution can be an appropriate model for processes where:

imported>Brianlenoach@hotmail.co.uk: flesh out the basic definition,s show the value and use of the Poisson distriburtion

2014-08-28T10:38:35Z

flesh out the basic definition,s show the value and use of the Poisson distriburtion

← Older revision		Revision as of 10:38, 28 August 2014
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	<i>Statistics</i>.		<i>Statistics</i>.

	A probability model used where discrete events occur in a ~~continuum~~.		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 full the probability distribution function of a Poisson random variable.
	For example, the number of business interruptions occurring in a given time period.		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. We intend to return to the latter example in a future editing session.


	The Poisson distribution can be an appropriate model for processes where:		The Poisson distribution can be an appropriate model for processes where:

imported>Doug Williamson: Amend from 'useful' model to 'appropriate'.

2014-08-07T11:19:06Z

Amend from 'useful' model to 'appropriate'.

← Older revision		Revision as of 11:19, 7 August 2014
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	The Poisson distribution can be ~~a useful~~ model for processes where:		The Poisson distribution can be an appropriate model for processes where:
	#Continuous observation is needed, rather than a finite 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.

imported>Doug Williamson: Add 'finite' before 'number' for clarity.

2014-08-07T11:11:36Z

Add 'finite' before 'number' for clarity.

← Older revision		Revision as of 11:11, 7 August 2014
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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

imported>Doug Williamson: Amend illustration from 'phone calls received' to 'business interruptions occurring'.

2014-08-07T11:10:14Z

Amend illustration from 'phone calls received' to 'business interruptions occurring'.

← Older revision		Revision as of 11:10, 7 August 2014
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	A probability model used where discrete events occur in a continuum.		A probability model used where discrete events occur in a continuum.

	For example, the number of ~~phone calls received~~ in a given time period.		For example, the number of business interruptions occurring in a given time period.

imported>Doug Williamson: Add 'with no upper limit' to differentiate from binomial distribution.

2014-08-07T11:06:36Z

Add 'with no upper limit' to differentiate from binomial distribution.

← Older revision		Revision as of 11:06, 7 August 2014
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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.

imported>Doug Williamson: Add 'integer' to 3.

2014-08-05T14:58:33Z

Add 'integer' to 3.

← Older revision		Revision as of 14:58, 5 August 2014
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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 value.		#The random variable takes a positive whole number (integer) value.
	#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.