Non-Parametric Recurrent Events Data Analysis - Revision history

Lisa Hacker: Redirected page to Recurrent Event Data Analysis

2012-06-25T10:53:42Z

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	~~{{Weibull Non-Parametric Recurrent Events Data Analysis}}~~		#REDIRECT [[Recurrent_Event_Data_Analysis]]

2012-06-20T09:47:12Z

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	{{Weibull Non-Parametric Recurrent Events Data ~~Analysiss~~}}		{{Weibull Non-Parametric Recurrent Events Data Analysis}}

2012-06-20T09:46:54Z

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	{{Weibull Non-Parametric Recurrent Events Data ~~Analysis~~}}		{{Weibull Non-Parametric Recurrent Events Data Analysiss}}

2012-02-14T00:41:05Z

Replaced content with '{{Weibull Non-Parametric Recurrent Events Data Analysis}}'

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			{{Weibull Non-Parametric Recurrent Events Data Analysis}}
	==Non-~~Parameteric Recurrence~~ Data Analysis==
	~~===Introduction===~~
	Non-parametric recurrence data analysis provides a nonparametric graphical estimate of the mean cumulative number or cost of recurrence per unit versus age. In the reliability field, the Mean Cumulative Function (MCF) can be used to: [31]

	~~:• Evaluate whether the population repair (or cost) rate increases or decreases with age (this is useful for product retirement and burn-in decisions).~~
	~~:• Estimate the average number or cost of repairs per unit during warranty or some time period.~~
	~~:• Compare two or more sets of data from different designs, production periods, maintenance policies, environments, operating conditions, etc.~~
	~~:• Predict future numbers and costs of repairs, such as, the next month, quarter, or year.~~
	~~:• Reveal unexpected information and insight.~~

	~~{{mean cumulative function for recurrence data}}~~

	~~{{confidence limits for the MCF~~}}

2012-02-14T00:36:28Z

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	~~{{Non-Parametric Recurrent Events Data Analysis}}~~

	==Non-Parameteric Recurrence Data Analysis==		==Non-Parameteric Recurrence Data Analysis==

2012-02-14T00:35:04Z

← Older revision		Revision as of 00:35, 14 February 2012
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	{{{{Non-Parametric Recurrent Events Data Analysis}}		{{Non-Parametric Recurrent Events Data Analysis}}

	==Non-Parameteric Recurrence Data Analysis==		==Non-Parameteric Recurrence Data Analysis==

2012-02-14T00:34:53Z

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			{{{{Non-Parametric Recurrent Events Data Analysis}}

	==Non-Parameteric Recurrence Data Analysis==		==Non-Parameteric Recurrence Data Analysis==
	===Introduction===		===Introduction===

2012-02-14T00:32:38Z

← Older revision		Revision as of 00:32, 14 February 2012
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	{{Non-~~Parametric Recurrent Events~~ Data Analysis}}		==Non-Parameteric Recurrence Data Analysis==
			===Introduction===
			Non-parametric recurrence data analysis provides a nonparametric graphical estimate of the mean cumulative number or cost of recurrence per unit versus age. In the reliability field, the Mean Cumulative Function (MCF) can be used to: [31]

			:• Evaluate whether the population repair (or cost) rate increases or decreases with age (this is useful for product retirement and burn-in decisions).
			:• Estimate the average number or cost of repairs per unit during warranty or some time period.
			:• Compare two or more sets of data from different designs, production periods, maintenance policies, environments, operating conditions, etc.
			:• Predict future numbers and costs of repairs, such as, the next month, quarter, or year.
			:• Reveal unexpected information and insight.

			{{mean cumulative function for recurrence data}}

			{{confidence limits for the MCF}}

2012-02-14T00:28:58Z

← Older revision		Revision as of 00:28, 14 February 2012
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	{{Non-Parametric Recurrent Events Data Analysis}}		{{Non-Parametric Recurrent Events Data Analysis}}

2012-02-14T00:28:26Z

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			{{Non-Parametric Recurrent Events Data Analysis}}
	{{~~recurrent events data analysis~~}}