ALTA Simumatic Data IPL-Lognormal: Difference between revisions
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Reliability analysis using simulation, in which reliability analyses are performed a large number of times on data sets that have been created using Monte Carlo simulation, can be a valuable tool for reliability practitioners. Such simulation analyses can assist the analyst to a) better understand life data analysis concepts, b) experiment with the influences of sample sizes and censoring schemes on analysis methods, c) construct simulation-based confidence intervals, d) better understand the concepts behind confidence intervals and e) design reliability tests. This section explores some of the results that can be obtained from simulation analyses with the SimuMatic utility in Weibull++. | Reliability analysis using simulation, in which reliability analyses are performed a large number of times on data sets that have been created using Monte Carlo simulation, can be a valuable tool for reliability practitioners. Such simulation analyses can assist the analyst to a) better understand life data analysis concepts, b) experiment with the influences of sample sizes and censoring schemes on analysis methods, c) construct simulation-based confidence intervals, d) better understand the concepts behind confidence intervals and e) design reliability tests. This section explores some of the results that can be obtained from simulation analyses with the SimuMatic utility in Weibull++. | ||
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| [http://help.synthesis8.com/weibull_alta8/alta_simumatic_setup_window.htm the help files...] | |||
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| [http://reliawiki.com/index.php/Inverse_Power_Law_(IPL)_Relationship the theory textbook...] | |||
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| [http://www.reliawiki.com/index.php/Temporary_needs_example_page use example(s)...] | |||
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Revision as of 22:52, 8 March 2012
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| Simumatic Data IPL-Lognormal |
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Reliability analysis using simulation, in which reliability analyses are performed a large number of times on data sets that have been created using Monte Carlo simulation, can be a valuable tool for reliability practitioners. Such simulation analyses can assist the analyst to a) better understand life data analysis concepts, b) experiment with the influences of sample sizes and censoring schemes on analysis methods, c) construct simulation-based confidence intervals, d) better understand the concepts behind confidence intervals and e) design reliability tests. This section explores some of the results that can be obtained from simulation analyses with the SimuMatic utility in Weibull++. |
Learn more from...
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the help files... |
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the theory textbook... |
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related article(s)... |
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use example(s)... |