Weibull++ Standard Folio Data 1P-Weibull: Difference between revisions
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The one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that shape parameter is known constant, | The one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that shape parameter is known constant, | ||
<math>\beta=C \,\!</math> | |||
or | or | ||
<math> 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. | 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 23:30, 10 February 2012
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The One-Parameter Weibull DistributionThe one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that shape parameter is known constant, [math]\displaystyle{ \beta=C \,\! }[/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. More... |
| See also The Weibull Distribution |
| See also Analysis Example |