Weibull++ Standard Folio Data 1P-Weibull: Difference between revisions

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::<math> R(t)=e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math>  
::<math> R(t)=e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math>  


where the only unknown parameter is the scale parameter, <math>\eta</math>. In this formulation we assume that the shape parameter is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.
In this formulation we assume that the shape parameter is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.




Revision as of 23:26, 10 February 2012

 

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The One-Parameter Weibull Distribution

The one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that

[math]\displaystyle{ \beta=C=Constant \,\! }[/math]

or

[math]\displaystyle{ R(t)=e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\! }[/math]

In this formulation we assume that the shape parameter is known a priori from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.


The Weibull Distribution
See an Example...


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