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 | where the only unknown parameter is the scale parameter, <math>\eta</math>. | ||
Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\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. | Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\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. | ||
Revision as of 23:24, 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
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... |