Template:Test of Life Comparison - Revision history

Richard House: Redirected page to Comparing Life Data Sets

2012-08-13T06:30:11Z

Redirected page to Comparing Life Data Sets

← Older revision		Revision as of 06:30, 13 August 2012
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	~~==Comparing Life Data Sets==~~		#REDIRECT [[Comparing_Life_Data_Sets]]
	It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The data sets could be from two alternate designs, manufacturers, lots, assembly lines, etc. Many methods are available in statistical literature for doing this when the units come from a ''complete'' sample (i.e., a sample with no censoring). This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods available in Weibull++ that are applicable to censored data.

	~~{{simple plotting}}~~

	~~{{using contour plots}}~~

	~~{{estimating equation using the cw}}~~

Lisa Hacker: /* Tests of Comparison */

2012-03-30T19:17:43Z

Tests of Comparison

← Older revision		Revision as of 19:17, 30 March 2012
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	==~~Tests of Comparison~~==		==Comparing Life Data Sets==
	It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The data sets could be from two alternate designs, manufacturers, lots, assembly lines, etc. Many methods are available in statistical literature for doing this when the units come from a ''complete'' sample (i.e., a sample with no censoring). This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods available in Weibull++ that are applicable to censored data.		It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The data sets could be from two alternate designs, manufacturers, lots, assembly lines, etc. Many methods are available in statistical literature for doing this when the units come from a ''complete'' sample (i.e., a sample with no censoring). This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods available in Weibull++ that are applicable to censored data.

Lisa Hacker: /* Tests of Comparison */

2012-03-30T17:52:13Z

Tests of Comparison

← Older revision		Revision as of 17:52, 30 March 2012
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	==Tests of Comparison==		==Tests of Comparison==
	It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The ~~units from which the~~ data ~~are obtained~~ could ~~either~~ be from two alternate designs, ~~alternate~~ manufacturers ~~or alternate~~ lots or assembly lines. Many methods are available in statistical literature for doing this when the units come from a complete sample, i.e. a sample with no censoring. This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods that are applicable to censored data~~, and are available in Weibull++~~.		It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The data sets could be from two alternate designs, manufacturers, lots, assembly lines, etc. Many methods are available in statistical literature for doing this when the units come from a ''complete'' sample (i.e., a sample with no censoring). This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods available in Weibull++ that are applicable to censored data.

	{{simple plotting}}		{{simple plotting}}

Harry Guo at 22:28, 27 February 2012

2012-02-27T22:28:38Z

← Older revision		Revision as of 22:28, 27 February 2012
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	{{using contour plots}}		{{using contour plots}}

			{{estimating equation using the cw}}

Harry Guo at 22:14, 27 February 2012

2012-02-27T22:14:49Z

← Older revision		Revision as of 22:14, 27 February 2012
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	==Tests of Comparison==		==Tests of Comparison==
	It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The units from which the data are obtained could either be from two alternate designs, alternate manufacturers or alternate lots or assembly lines. Many methods are available in statistical literature for doing this when the units come from a complete sample, i.e. a sample with no censoring. This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods that are applicable to censored data, and are available in Weibull++.		It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The units from which the data are obtained could either be from two alternate designs, alternate manufacturers or alternate lots or assembly lines. Many methods are available in statistical literature for doing this when the units come from a complete sample, i.e. a sample with no censoring. This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods that are applicable to censored data, and are available in Weibull++.

	{{simple plotting}}		{{simple plotting}}

	{{using contour plots}}		{{using contour plots}}

Harry Guo: Created page with '==Tests of Comparison== It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life di…'

2012-02-14T00:52:22Z

Created page with '==Tests of Comparison== It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life di…'

New page

==Tests of Comparison==
It is often desirable to be able to compare two sets of reliability or life data in order to determine which of the data sets has a more favorable life distribution. The units from which the data are obtained could either be from two alternate designs, alternate manufacturers or alternate lots or assembly lines. Many methods are available in statistical literature for doing this when the units come from a complete sample, i.e. a sample with no censoring. This process becomes a little more difficult when dealing with data sets that have censoring, or when trying to compare two data sets that have different distributions. In general, the problem boils down to that of being able to determine any statistically significant difference between the two samples of potentially censored data from two possibly different populations. This section discusses some of the methods that are applicable to censored data, and are available in Weibull++.
{{simple plotting}}

{{using contour plots}}