Additional Tools - Revision history

Lisa Hacker: /* Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right]\,\! Using the Life Comparison Wizard */

2023-03-16T00:33:57Z

Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right]\,\! Using the Life Comparison Wizard

← Older revision		Revision as of 00:33, 16 March 2023
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	One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.		One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.

	===~~Estimating <math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]\,\!</math>~~ Using the Life Comparison Wizard===		===Using the Life Comparison Wizard===
	Another methodology, suggested by Gerald G. Brown and Herbert C. Rutemiller, is to estimate the probability of whether the times-to-failure of one population are better or worse than the times-to-failure of the second. The equation used to estimate this probability is given by:		Another methodology, suggested by Gerald G. Brown and Herbert C. Rutemiller, is to estimate the probability of whether the times-to-failure of one population are better or worse than the times-to-failure of the second. The equation used to estimate this probability is given by:

Kate Racaza: /* Degradation Analysis */

2015-12-01T18:23:51Z

Degradation Analysis

← Older revision		Revision as of 18:23, 1 December 2015
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	One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.		One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.

			<div class="noprint">
	{{Examples Box\|ALTA_Examples\|<p>More application examples are available! See also:</p>		{{Examples Box\|ALTA_Examples\|<p>More application examples are available! See also:</p>
	{{Examples Both\|http://www.reliasoft.com/alta/examples/rc3/index.htm\|Accelerated Degradation\|http://www.reliasoft.tv/alta/appexamples/alta_app_ex_4.html\|Watch the video...}}<nowiki/>		{{Examples Both\|http://www.reliasoft.com/alta/examples/rc3/index.htm\|Accelerated Degradation\|http://www.reliasoft.tv/alta/appexamples/alta_app_ex_4.html\|Watch the video...}}<nowiki/>
	}}		}}
			</div>

Richard House at 22:49, 21 September 2012

2012-09-21T22:49:23Z

Richard House: /* Common Shape Parameter Likelihood Ratio Test */

2012-09-05T15:50:23Z

Common Shape Parameter Likelihood Ratio Test

← Older revision		Revision as of 15:50, 5 September 2012
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	{{template:ALTABOOK\|11}}		{{template:ALTABOOK\|11}}
	==Common Shape Parameter Likelihood Ratio Test==		==Common Shape Parameter Likelihood Ratio Test==
	In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized Nelson [[Appendix_E:_References\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.		In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized, as described in Nelson [[Appendix_E:_References\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.

	The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math> , is calculated as follows:		The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math> , is calculated as follows:

Richard House: /* Common Shape Parameter Likelihood Ratio Test */

2012-09-04T20:35:34Z

Common Shape Parameter Likelihood Ratio Test

← Older revision		Revision as of 20:35, 4 September 2012
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	{{template:ALTABOOK\|11}}		{{template:ALTABOOK\|11}}
	==Common Shape Parameter Likelihood Ratio Test==		==Common Shape Parameter Likelihood Ratio Test==
	In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized [[~~Reference Appendix D~~: ~~References~~\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.		In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized Nelson [[Appendix_E:_References\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.

	The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math> , is calculated as follows:		The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math> , is calculated as follows:

Richard House: /* Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right] Using the Life Comparison Wizard */

2012-08-29T23:22:08Z

Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right] Using the Life Comparison Wizard

← Older revision		Revision as of 23:22, 29 August 2012
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	::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt</math>		::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt</math>

	where <math>{{f}_{1}}(t)\,\!</math> is the ~~<math>~~pdf~~</math>~~ of the first distribution and <math>{{R}_{2}}(t)\,\!</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.		where <math>{{f}_{1}}(t)\,\!</math> is the ''pdf'' of the first distribution and <math>{{R}_{2}}(t)\,\!</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.

	For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t\,\!</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:		For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t\,\!</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:

Richard House: /* Tests of Comparison */

2012-08-22T04:43:03Z

Tests of Comparison

← Older revision		Revision as of 04:43, 22 August 2012
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	==Tests of Comparison==		==Tests of Comparison==
	It is often desirable to be able to compare two sets of accelerated 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 ALTA.		It is often desirable to be able to compare two sets of accelerated 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 ALTA.


	===Simple Plotting===		===Simple Plotting===
	One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.		One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.


	===Estimating <math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]</math> Using the Life Comparison Wizard===		===Estimating <math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]</math> Using the Life Comparison Wizard===

Richard House: /* Degradation Analysis */

2012-08-22T04:42:23Z

Degradation Analysis

← Older revision		Revision as of 04:42, 22 August 2012
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	\end{matrix}</math></center>		\end{matrix}</math></center>

	where <math>y</math> represents the performance, <math>x</math> represents time, and <math>a</math> and <math>b</math> are model parameters to be solved for.		where <math>y\,\!</math> represents the performance, <math>x\,\!</math> represents time, and <math>a\,\!</math> and <math>b\,\!</math> are model parameters to be solved for.
	Once the model parameters <math>{{a}_{i}}</math> and <math>{{b}_{i}}</math> (and <math>{{c}_{i}}</math> for Lloyd-Lipow) are estimated for each sample <math>i</math> , a time, <math>{{x}_{i}},</math> can be extrapolated that corresponds to the defined level of failure <math>y</math> . The computed <math>{{x}_{i}}</math> can now be used as our times-to-failure for subsequent accelerated life data analysis. As with any sort of extrapolation, one must be careful not to extrapolate too far beyond the actual range of data in order to avoid large inaccuracies (modeling errors).		Once the model parameters <math>{{a}_{i}}\,\!</math> and <math>{{b}_{i}}\,\!</math> (and <math>{{c}_{i}}\,\!</math> for Lloyd-Lipow) are estimated for each sample <math>i\,\!</math> , a time, <math>{{x}_{i}},\,\!</math> can be extrapolated that corresponds to the defined level of failure <math>y\,\!</math> . The computed <math>{{x}_{i}}\,\!</math> can now be used as our times-to-failure for subsequent accelerated life data analysis. As with any sort of extrapolation, one must be careful not to extrapolate too far beyond the actual range of data in order to avoid large inaccuracies (modeling errors).

	One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.		One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.

Richard House: /* Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right] Using the Life Comparison Wizard */

2012-08-22T04:40:49Z

Estimating P\left[ {{t}_{2}}\ge {{t}_{1}} \right] Using the Life Comparison Wizard

← Older revision		Revision as of 04:40, 22 August 2012
Line 35:		Line 35:
	::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt</math>		::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt</math>

	where <math>{{f}_{1}}(t)</math> is the <math>pdf</math> of the first distribution and <math>{{R}_{2}}(t)</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.		where <math>{{f}_{1}}(t)\,\!</math> is the <math>pdf</math> of the first distribution and <math>{{R}_{2}}(t)\,\!</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.

	For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:		For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t\,\!</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:

	*If <math>P=0.50</math> , then the statement is equivalent to saying that both X and Y are equal.		*If <math>P=0.50\,\!</math> , then the statement is equivalent to saying that both X and Y are equal.

	*If <math>P<0.50</math> or, for example, <math>P=0.10</math> , then the statement is equivalent to saying that <math>P=1-0.10=0.90</math> , or Y is better than X with a 90% probability.		*If <math>P<0.50\,\!</math> or, for example, <math>P=0.10\,\!</math> , then the statement is equivalent to saying that <math>P=1-0.10=0.90\,\!</math> , or Y is better than X with a 90% probability.

	ALTA's Comparison Wizard allows you to perform such calculations. The comparison is performed at the given use stress levels of each data set, using the equation:		ALTA's Comparison Wizard allows you to perform such calculations. The comparison is performed at the given use stress levels of each data set, using the equation:

	::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t,{{V}_{Use,1}})\cdot {{R}_{2}}(t,{{V}_{Use,2}})\cdot dt</math>		::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t,{{V}_{Use,1}})\cdot {{R}_{2}}(t,{{V}_{Use,2}})\cdot dt</math>


	==Degradation Analysis==		==Degradation Analysis==

Richard House: /* Common Shape Parameter Likelihood Ratio Test */

2012-08-22T04:36:04Z

Common Shape Parameter Likelihood Ratio Test

← Older revision		Revision as of 04:36, 22 August 2012
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	Once the LR statistic has been calculated, then:		Once the LR statistic has been calculated, then:

	*If <math>T\le {{\chi }^{2}}(1-\alpha ;n-1),</math> the <math>n</math> shape parameter estimates do not differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.		*If <math>T\le {{\chi }^{2}}(1-\alpha ;n-1),</math> the <math>n\,\!</math> shape parameter estimates do not differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.

	*If <math>T>{{\chi }^{2}}(1-\alpha ;n-1),\,\!</math> the <math>n</math> shape parameter estimates differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.		*If <math>T>{{\chi }^{2}}(1-\alpha ;n-1),\,\!</math> the <math>n\,\!</math> shape parameter estimates differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.

← Older revision		Revision as of 22:49, 21 September 2012
Line 1:		Line 1:
	{{template:ALTABOOK\|11}}		{{template:ALTABOOK\|11}}
	==Common Shape Parameter Likelihood Ratio Test==		==Common Shape Parameter Likelihood Ratio Test==
	In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized, as described in Nelson [[Appendix_E:_References\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.		In order to assess the assumption of a common shape parameter among the data obtained at various stress levels, the likelihood ratio (LR) test can be utilized, as described in Nelson [[Appendix_E:_References\|[28]]]. This test applies to any distribution with a shape parameter. In the case of ALTA, it applies to the Weibull and lognormal distributions. When Weibull is used as the underlying life distribution, the shape parameter, <math>\beta ,\,\!</math> is assumed to be constant across the different stress levels (i.e., stress independent). Similarly, <math>{{\sigma }_{{{T}'}}}\,\!</math>, the parameter of the lognormal distribution is assumed to be constant across the different stress levels.

	The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math> , is calculated as follows:		The likelihood ratio test is performed by first obtaining the LR test statistic, <math>T\,\!</math>. If the true shape parameters are equal, then the distribution of <math>T\,\!</math> is approximately chi-square with <math>n-1\,\!</math> degrees of freedom, where <math>n\,\!</math> is the number of test stress levels with two or more exact failure points. The LR test statistic, <math>T\,\!</math>, is calculated as follows:

	::<math>T=2({{\hat{\Lambda }}_{1}}+...+{{\hat{\Lambda }}_{n}}-{{\hat{\Lambda }}_{0}})</math>		::<math>T=2({{\hat{\Lambda }}_{1}}+...+{{\hat{\Lambda }}_{n}}-{{\hat{\Lambda }}_{0}})\,\!</math>

	where <math>\hat{\Lambda}_{1}, ..., \hat{\Lambda}_{n}\,\!</math> are the likelihood values obtained by fitting a separate distribution to the data from each of the <math>n\,\!</math> test stress levels (with two or more exact failure times). The likelihood value, <math>{{\hat{\Lambda }}_{0}},\,\!</math> is obtained by fitting a model with a common shape parameter and a separate scale parameter for each of the <math>n\,\!</math> stress levels, using indicator variables.		where <math>\hat{\Lambda}_{1}, ..., \hat{\Lambda}_{n}\,\!</math> are the likelihood values obtained by fitting a separate distribution to the data from each of the <math>n\,\!</math> test stress levels (with two or more exact failure times). The likelihood value, <math>{{\hat{\Lambda }}_{0}},\,\!</math> is obtained by fitting a model with a common shape parameter and a separate scale parameter for each of the <math>n\,\!</math> stress levels, using indicator variables.

	Once the LR statistic has been calculated, then:		Once the LR statistic has been calculated, then:

	*If <math>T\le {{\chi }^{2}}(1-\alpha ;n-1),</math> the <math>n\,\!</math> shape parameter estimates do not differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.		*If <math>T\le {{\chi }^{2}}(1-\alpha ;n-1),\,\!</math> the <math>n\,\!</math> shape parameter estimates do not differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.

	*If <math>T>{{\chi }^{2}}(1-\alpha ;n-1),\,\!</math> the <math>n\,\!</math> shape parameter estimates differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.		*If <math>T>{{\chi }^{2}}(1-\alpha ;n-1),\,\!</math> the <math>n\,\!</math> shape parameter estimates differ statistically significantly at the 100 <math>\alpha %\,\!</math> level.


	<math>{{\chi }^{2}}(1-\alpha ;n-1)\,\!</math> is the 100(1- <math>\alpha \,\!)</math> percentile of the chi-square distribution with <math>n-1\,\!</math> degrees of freedom.		<math>{{\chi }^{2}}(1-\alpha ;n-1)\,\!</math> is the 100(1- <math>\alpha)\,\!</math> percentile of the chi-square distribution with <math>n-1\,\!</math> degrees of freedom.


Line 28:		Line 28:
	One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.		One popular graphical method for making this determination involves plotting the data at a given stress level with confidence bounds and seeing whether the bounds overlap or separate at the point of interest. This can be effective for comparisons at a given point in time or a given reliability level, but it is difficult to assess the overall behavior of the two distributions, as the confidence bounds may overlap at some points and be far apart at others. This can be easily done using the overlay plot feature in ALTA.

	===Estimating <math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]</math> Using the Life Comparison Wizard===		===Estimating <math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]\,\!</math> Using the Life Comparison Wizard===
	Another methodology, suggested by Gerald G. Brown and Herbert C. Rutemiller, is to estimate the probability of whether the times-to-failure of one population are better or worse than the times-to-failure of the second. The equation used to estimate this probability is given by:		Another methodology, suggested by Gerald G. Brown and Herbert C. Rutemiller, is to estimate the probability of whether the times-to-failure of one population are better or worse than the times-to-failure of the second. The equation used to estimate this probability is given by:

	::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt</math>		::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t)\cdot {{R}_{2}}(t)\cdot dt\,\!</math>

	where <math>{{f}_{1}}(t)\,\!</math> is the ''pdf'' of the first distribution and <math>{{R}_{2}}(t)\,\!</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.		where <math>{{f}_{1}}(t)\,\!</math> is the ''pdf'' of the first distribution and <math>{{R}_{2}}(t)\,\!</math> is the reliability function of the second distribution. The evaluation of the superior data set is based on whether this probability is smaller or greater than 0.50. If the probability is equal to 0.50, then is equivalent to saying that the two distributions are identical.

	For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t\,\!</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:		For example, consider two alternate designs, where X and Y represent the life test data from each design. If we simply wanted to choose the component with the higher reliability, we could simply select the component with the higher reliability at time <math>t\,\!</math>. However, if we wanted to design the product to be as long-lived as possible, we would want to calculate the probability that the entire distribution of one design is better than the other. The statement "the probability that X is greater than or equal to Y" can be interpreted as follows:

	*If <math>P=0.50\,\!</math> , then the statement is equivalent to saying that both X and Y are equal.		*If <math>P=0.50\,\!</math>, then the statement is equivalent to saying that both X and Y are equal.

	*If <math>P<0.50\,\!</math> or, for example, <math>P=0.10\,\!</math> , then the statement is equivalent to saying that <math>P=1-0.10=0.90\,\!</math> , or Y is better than X with a 90% probability.		*If <math>P<0.50\,\!</math> or, for example, <math>P=0.10\,\!</math>, then the statement is equivalent to saying that <math>P=1-0.10=0.90\,\!</math>, or Y is better than X with a 90% probability.

	ALTA's Comparison Wizard allows you to perform such calculations. The comparison is performed at the given use stress levels of each data set, using the equation:		ALTA's Comparison Wizard allows you to perform such calculations. The comparison is performed at the given use stress levels of each data set, using the equation:

	::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t,{{V}_{Use,1}})\cdot {{R}_{2}}(t,{{V}_{Use,2}})\cdot dt</math>		::<math>P\left[ {{t}_{2}}\ge {{t}_{1}} \right]=\int_{0}^{\infty }{{f}_{1}}(t,{{V}_{Use,1}})\cdot {{R}_{2}}(t,{{V}_{Use,2}})\cdot dt\,\!</math>

	==Degradation Analysis==		==Degradation Analysis==
Line 58:		Line 58:
	Gompertz\ \ : & y=a\cdot {{b}^{cx}} \\		Gompertz\ \ : & y=a\cdot {{b}^{cx}} \\
	Lloyd-Lipow\ \ : & y=a-b/x \\		Lloyd-Lipow\ \ : & y=a-b/x \\
	\end{matrix}</math></center>		\end{matrix}\,\!</math></center>

	where <math>y\,\!</math> represents the performance, <math>x\,\!</math> represents time, and <math>a\,\!</math> and <math>b\,\!</math> are model parameters to be solved for.		where <math>y\,\!</math> represents the performance, <math>x\,\!</math> represents time, and <math>a\,\!</math> and <math>b\,\!</math> are model parameters to be solved for.
	Once the model parameters <math>{{a}_{i}}\,\!</math> and <math>{{b}_{i}}\,\!</math> (and <math>{{c}_{i}}\,\!</math> for Lloyd-Lipow) are estimated for each sample <math>i\,\!</math> , a time, <math>{{x}_{i}},\,\!</math> can be extrapolated that corresponds to the defined level of failure <math>y\,\!</math> . The computed <math>{{x}_{i}}\,\!</math> can now be used as our times-to-failure for subsequent accelerated life data analysis. As with any sort of extrapolation, one must be careful not to extrapolate too far beyond the actual range of data in order to avoid large inaccuracies (modeling errors).		Once the model parameters <math>{{a}_{i}}\,\!</math> and <math>{{b}_{i}}\,\!</math> (and <math>{{c}_{i}}\,\!</math> for Lloyd-Lipow) are estimated for each sample <math>i\,\!</math>, a time, <math>{{x}_{i}},\,\!</math> can be extrapolated that corresponds to the defined level of failure <math>y\,\!</math>. The computed <math>{{x}_{i}}\,\!</math> can now be used as our times-to-failure for subsequent accelerated life data analysis. As with any sort of extrapolation, one must be careful not to extrapolate too far beyond the actual range of data in order to avoid large inaccuracies (modeling errors).

	One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.		One may also define a censoring time past which no failure times are extrapolated. In practice, there is usually a rather narrow band in which this censoring time has any practical meaning. With a relatively low censoring time, no failure times will be extrapolated, which defeats the purpose of degradation analysis. A relatively high censoring time would occur after all of the theoretical failure times, thus being rendered meaningless. Nevertheless, certain situations may arise in which it is beneficial to be able to censor the accelerated degradation data.