Real World Statistics: What to Do When It`s Not a Bell Curve 2017

Topics

• Subjective and quantitative methods exist to test the assumption that data follow the normal (or other selected) distribution
• He normal or bell curve distribution is far more common in textbooks than it is in real factories
• SPC charts can be created that work properly for non-normal distributions and have the same false alarm risk as the traditional Shewhart chart for a normal distribution
• When the distribution might be non-normal. Processes with unilateral specification limits at one end and physical limits at the other-e.g. an upper specification limit for an impurity and the fact that it is impossible to get less than zero impurities-often signifies that the distribution will not be a bell curve
• Process performance indices can be calculated that reflect accurately the nonconforming fraction (or defects per million opportunities) for non-normal distributions

Who should Attend

Attendees from:

• Quality Engineers
• Manufacturing
• Managers