Template:Example: Published 2P Weibull Distribution Interval Data MLE Example: Difference between revisions
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[[Image:example16table.png|center|800px]] | [[Image:example16table.png|center|800px]] | ||
'''Published Results:''' | '''Published Results:''' | ||
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Published results (using MLE): | Published results (using MLE): | ||
[[Image:example16formula.png | [[Image:example16formula.png]] | ||
Published 95% FM confidence limits on the parameters: | Published 95% FM confidence limits on the parameters: | ||
[[Image:example16formula2.png | [[Image:example16formula2.png]] | ||
Published variance/covariance matrix: | Published variance/covariance matrix: | ||
[[Image:example16formula3.png | [[Image:example16formula3.png]] | ||
'''Computed Results in Weibull++''' | '''Computed Results in Weibull++''' | ||
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Weibull++ computed parameters for maximum likelihood are: | Weibull++ computed parameters for maximum likelihood are: | ||
[[Image:compexample16formula.png | [[Image:compexample16formula.png]] | ||
Weibull++ computed 95% FM confidence limits on the parameters: | Weibull++ computed 95% FM confidence limits on the parameters: | ||
[[Image:compexample16formula2.png | [[Image:compexample16formula2.png]] | ||
Weibull++ computed/variance covariance matrix: | Weibull++ computed/variance covariance matrix: | ||
[[Image:compexample16formula3.png | [[Image:compexample16formula3.png]] | ||
Revision as of 05:20, 6 August 2012
Published 2P Weibull Distribution Interval Data MLE Example
From Wayne Nelson, Applied Life Data Analysis, Page 415 [30]. One hundred and sixty-seven (167) identical parts were inspected for cracks. The following is a table of their last inspection times and times-to-failure:
Published Results:
Published results (using MLE):
Published 95% FM confidence limits on the parameters:
Published variance/covariance matrix:
Computed Results in Weibull++
This same data set can be entered into Weibull++ by selecting the data sheet Times to Failure, with Right Censored Data (Suspensions), with Interval and Left Censored Data and with Grouped Observations options, and using MLE.
Weibull++ computed parameters for maximum likelihood are:
Weibull++ computed 95% FM confidence limits on the parameters:
Weibull++ computed/variance covariance matrix:
