Simulation with RGA Models: Difference between revisions
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When analyzing developmental systems for reliability growth and conducting data analysis of fielded repairable systems, it is often useful to experiment with various what if scenarios or put together hypothetical analyses before having available test data in order to find the best way to analyze data when they become available. With that in mind, RGA 7 offers applications based on Monte Carlo simulation that can be used in order to: | |||
<br> | |||
<br> | |||
:a) better understand reliability growth concepts. | |||
:b) experiment with the impact of sample size, test time and growth parameters on analysis results. | |||
:c) construct simulation-based confidence intervals. | |||
:d) better understand concepts behind confidence intervals. | |||
:e) design reliability demonstration tests. | |||
{{ | There are two applications of the Monte Carlo simulation in RGA 7. One is called Generate Monte Carlo Data and the other is called SimuMatic. | ||
<br> | |||
<br> | |||
{{generate monte carlo data}} | |||
{{Simumatic rsa}} | |||
Revision as of 07:11, 23 August 2012
When analyzing developmental systems for reliability growth and conducting data analysis of fielded repairable systems, it is often useful to experiment with various what if scenarios or put together hypothetical analyses before having available test data in order to find the best way to analyze data when they become available. With that in mind, RGA 7 offers applications based on Monte Carlo simulation that can be used in order to:
- a) better understand reliability growth concepts.
- b) experiment with the impact of sample size, test time and growth parameters on analysis results.
- c) construct simulation-based confidence intervals.
- d) better understand concepts behind confidence intervals.
- e) design reliability demonstration tests.
There are two applications of the Monte Carlo simulation in RGA 7. One is called Generate Monte Carlo Data and the other is called SimuMatic.
When analyzing developmental systems for reliability growth and conducting data analysis of fielded repairable systems, it is often useful to experiment with various what if scenarios or put together hypothetical analyses before having available test data in order to find the best way to analyze data when they become available. With that in mind, RGA 7 offers applications based on Monte Carlo simulation that can be used in order to:
- a) better understand reliability growth concepts.
- b) experiment with the impact of sample size, test time and growth parameters on analysis results.
- c) construct simulation-based confidence intervals.
- d) better understand concepts behind confidence intervals.
- e) design reliability demonstration tests.
There are two applications of the Monte Carlo simulation in RGA 7. One is called Generate Monte Carlo Data and the other is called SimuMatic.
Template loop detected: Template:Generate monte carlo data
Template loop detected: Template:Simumatic rsa
When analyzing developmental systems for reliability growth and conducting data analysis of fielded repairable systems, it is often useful to experiment with various what if scenarios or put together hypothetical analyses before having available test data in order to find the best way to analyze data when they become available. With that in mind, RGA 7 offers applications based on Monte Carlo simulation that can be used in order to:
- a) better understand reliability growth concepts.
- b) experiment with the impact of sample size, test time and growth parameters on analysis results.
- c) construct simulation-based confidence intervals.
- d) better understand concepts behind confidence intervals.
- e) design reliability demonstration tests.
There are two applications of the Monte Carlo simulation in RGA 7. One is called Generate Monte Carlo Data and the other is called SimuMatic.
Template loop detected: Template:Generate monte carlo data
Template loop detected: Template:Simumatic rsa