ALTA Simumatic Data PPH-Exponential: 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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valign="middle" | [http://www.reliawiki.com/index.php/Template:PH_Model#Proportional_Hazards_Model Proportional Hazards Model]
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| [http://reliawiki.com/index.php/Multivariable_Relationships:_General_Log-Linear_and_Proportional_Hazards the theory textbook...]  
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| [http://www.reliawiki.com/index.php/Simumatic_Articles related article(s)...]
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| [http://www.reliawiki.com/index.php/Temporary_needs_example_page use example(s)...]
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Revision as of 23:21, 8 March 2012

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Simumatic Data PPH-Exponential

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...

Helpblue.png the help files...
Book blue.png the theory textbook...
Articleblue.png related article(s)...
Bulbblue.png use example(s)...
















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