This looks quite exhaustive, please advise.
- ANOVA Reliability Growth Modeling
- Chi-Square Response Surface Modeling
- Regression Time Series Analysis
- Logistic Regression Hypothesis Testing
- Dummy Variable Regression Logit
- Bayesian Belief Network Monte Carlo Simulation
- Designed Experiments Optimization
- Discrete Event Simulation
- Reliability Growth Modeling
- Response Surface Modeling
- Time Series Analysis
- Hypothesis Testing
- Logit
- Discrete Event Simulation
So what would be very helpful for you is to either get some training on data analysis and statistical techniques or hire a statistician to help you with your High Maturity efforts. Then you will figure out which quantitative analysis technique(s) are appropriate for your data and your organization.
And, to quote Pat O'Toole: "High maturity is NOT just about statistical techniques. Rather, it is about performing your critical processes so consistently that the information to be gleaned from the use of these techniques contain more signal than noise. You can use the data streaming off the critical processes to detect abnormal performance, and to predict (in a statistical sense) future outcomes of interest."
2 comments:
One quick thought on the statement "neither the CMMI nor the SEI expect that all organizations must use these techniques".
Even though these techniques are not "must" and should be seen as a means to an end, on the ground, they are taken as "must" by the CMMI Folks - Implementers, Consultants, and Appraisers. IMHO one way to makes this appear natural is to dove-tail CMMI High Maturity and Six Sigma. Pls see the post below from my website connect2hcb:
CMMI High Maturity and Six Sigma
Dear connect2hcb,
I would agree with you in general. But again, the list of techniques are all good candidates for use at ML 4 and 5, but they are NOT required NOR expected that they must be used.
There is a very good fit between the CMMI and Six Sigma at ML 4 and 5. And I would also contend that if you try to use Six Sigma at ML 2 or ML 3, you run the risk of fixing one process and appear to achieve your goal while breaking one or more other processes that can be more costly than what you have "fixed." To use Six Sigma properly, you need to have well known and understood, stable processes that can be described by using data.
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