When using historical data for regression analysis, the variables cannot be controlled.  Regression with historical data merely tells you the mathematical relationship between variables.  Bias could be unknowingly introduced by using data that was simply available.  For example, two variables may always be changed together and the effects may not be apparent. 

 

When using a DOE data to run a regression, the variables are controlled and the factors are kept independent of one another.  Interactions can be observed and understood.  Parameters can be changed and tests can be ran to verify the optimization of the model.  Noise can also be accounted for, something that using historical data does not account for.

 

Overall, DOE is the preferred method but may not always be practical to perform due to cost, time or issues with data collection.  The benefits include understanding how the variables interact with each other and the sensitivity at which they interact.  However, if the goal is to understand the correlation between variables (and not the interactions), historical data may be acceptable. 

Using a Z score allows comparison between two samples.  They assume a normal distribution and can help describe how far above or below the average the data are.

A cause can be considered a root cause if it satisfies the below items:

1. The cause is clearly linked to the symptoms of the problem.
2. It is at the lowest actionable level.  If action cannot be taken against the root cause, it is no longer productive to continue going lower in the analysis. 
3. Changes to the identified cause will not create additional problems or problems that are deemed unacceptable.
4. Multiple causes have been considered and the selected root cause(s) is the best fit for the problem.

You may choose to not change x if the business case cannot be made.  It may not be practical to make changes to the X.  Statistical significance is not the same as practical significance.

You can use AAA for events such as:

• Interviewing / hiring
• Appraisals (car, jewelry, etc)
• Defining / improving acceptance criteria
• Any time a decision must be made with qualitative data; commonly used in coding of the data

Probability can be used to predict cycle, completion, response, and delay times.  It can also be used to predict how much rework or repair may be needed.  This can be used in planning and estimation activities.