Weibull++ Standard Folio Data 1P-Weibull: Difference between revisions
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The one-parameter Weibull distribution is a special case of | The one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that | ||
::<math>\beta=C=Constant \,\!</math> | ::<math>\beta=C=Constant \,\!</math> | ||
or | |||
::<math> f(t)={ \frac{C}{\eta }}\left( {\frac{t}{\eta }}\right) ^{C-1}e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math> | |||
<math> f(t)={ \frac{C}{\eta }}\left( {\frac{t}{\eta }}\right) ^{C-1}e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math> | |||
where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>. | where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>. |
Revision as of 23:23, 10 February 2012
The One-Parameter Weibull DistributionThe one-parameter Weibull distribution is a special case of the two parameter Weibull that assumes that
or
where the only unknown parameter is the scale parameter, [math]\displaystyle{ \eta\,\! }[/math]. Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter [math]\displaystyle{ \beta \,\! }[/math] 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.
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The Weibull Distribution |
See an Example... |