I am trying to create a process in which I can identify if a process is out of control.
My idea was to do something similar to 6 sigma, where when a point is outside of the mean by +-3 sigma, then its considered out of control.
However, my process is not normal which is throwing me off.
The process involves counting the number of times that a certain event happens on a day (60%+ of the time the count is 0).
So the process looks like an exponential distribution and I want to draw conclusions from the model However: 1) its not continuous, but discrete - I dont think this is an issue 2) the data is not from arrival times or something else but rather a count - I dont think this is an issue.
So, I can fit the data to an exponential model and obtain a lambda, but now what?
Questions: 1) In general, if the data fits a given distribution (assuming all assumptions are met) does it matter that the data is unconventional for that distribution in regards for using the model to draw conclusions? 2) How do I use this model to actually determine if new points are out of control? Is it even appropriate? Should I be looking elsewhere? Thanks.