working with Non normal data

hi,
very basic question i have is how you are able to plot Histogram for Attribute data & hence check Normality.
Atrribute data does not follow normal distribution curve, they will either follow Poission or Binomial curve.
 

How can we say that No. of tickets is a discrete data ? No. of tickets will be in integer …..
I think its a variable data and should not be treated as a discrete.
Try to remove the outliers from the data and then check for normality. try to find out the special causes for these outliers.
I hope it will work in your case.
Thanks,
Prasoon

Lou:
First of all, what kind of data are in the tickets? I assume you are analyzing continuous data (e.g. length values).
Second, if you are picking the trouble tickets you are not taking a random sample of the process. It is likely that you are picking the tails of the parent population. Even in the case that the parent population is normally distributed, you can not expect that such sample shows normality.
Third, why do you need to transform this data to be normally distributed? I will be justified if you want to apply an statistical test or chart which requires normallity. Is this the case?  I assume that your real objective is to discover why parts are out of spec and therefore trouble tickets are rised. Then I would suggest to plot scatter Final quality variable vs process parameters (e.g CTQ vs CTP) and then look for a pattern of the trouble tickets.
I hope it helps. Good luck,
Arturo

Lou,First, do you trust your data ?Have you done an MSA on the data you deal with ?Have you taken into account the “Saturday” and “Sunday” ?Do you have benchmark to compare your baseline against ?If you suspect an outlier being the cause of the non-normal distribution, find it, understand it and isolate (remove) it from your dataset and re-run your chart.This will help you in obtaining a normal distribution.This will not give you any indication of why these tickets were created or how to prevent them.
Prasoon, Anand,The “Discrete Data” definition below can be found at:https://www.isixsigma.com/ dictionary/ Discrete_Data-226.htm
Discrete DataDiscrete data is information that can be categorized into a classification. Discrete data is based on counts. Only a finite number of values is possible, and the values cannot be subdivided meaningfully. For example, the number of parts damaged in shipment.Attribute data (aka Discrete data) is data that can’t be broken down into a smaller unit and add additional meaning. It is typically things counted in whole numbers. There is no such thing as ‘half a defect.’ Population data is attribute because you are generally counting people and putting them into various catagories (i.e. you are counting their ‘attributes’). I know, you were about to ask about the ‘2.4 kids’ statistic when they talk about average house holds. But that actually illustrates my point. Who ever heard of .4 of a person. It doesn’t really add addition ‘meaning’ to the description.See Continuous Data for alternative data type.Observations made by categorizing subjects so that there is a distinct interval between any two possible values. “Good or Bad” and “Tall or Short”Matthieu

  Let’s back up a bit here.
You said, “I am working on a project to reduce the # of trouble tickets in a specific category. I have a sample of trouble tickets (TTs) per day for a 30 day period.”
What we need are more details.
1. Give us a definition of a trouble ticket – better yet give us an example.  Is this some kind of pass/fail or is it some kind of check list or?
2. Does this specific category have only a single thing that will result in the issuing of a trouble ticket or does it have multiple things that could result in a trouble ticket?
3. If there are multiple things within this specific category that could generate a trouble ticket – where’s your bean count by thing (the pareto chart)?  What does it look like?
4. Since you are working to reduce the number of trouble tickets one would assume you would want to look at frequency of trouble tickets against things like time of day, day of the week, shift changes over time, production lines across time, raw material changes over time, etc. In short – what does this data look like when plotted against things that might show trending or clustering which in turn might suggest possible cause and effect?
 Histograms are interesting but, based on what you have posted so far, I don’t see why you would even care about them or any of their characteristics at this point.
 

Robert,
      Thanks for your feedback. Being new to SS I guess I was hell bent on having normal data to work with, not realizing that there will be times that I will have non normal data. To answer some of your questions:
1. Give us a definition of a trouble ticket – better yet give us an example.  Is this some kind of pass/fail or is it some kind of check list or?
In this case the, category of trouble tickets are for MS Outlook. Some examples: I can’t access my email, I am missing emails, etc…
2. Does this specific category have only a single thing that will result in the issuing of a trouble ticket or does it have multiple things that could result in a trouble ticket?
There could be a number of things wrong with Outlook that would cause a customer to open a TT.
3. If there are multiple things within this specific category that could generate a trouble ticket – where’s your bean count by thing (the pareto chart)?  What does it look like?
When I created the pareto chart, it clearly highlighted the major problem areas causing TTs to be opened. In the case there were about 5 areas that made up the 80%. We’re beginning to focus on identifying probable causes for each of those areas.
4. Since you are working to reduce the number of trouble tickets one would assume you would want to look at frequency of trouble tickets against things like time of day, day of the week, shift changes over time, production lines across time, raw material changes over time, etc. In short – what does this data look like when plotted against things that might show trending or clustering which in turn might suggest possible cause and effect?
That’s a great question. When I collected my data I was simply concentrating on how many TTs had been opened in a 30 day period. I had a break down of TTs per day for that time frame, but didn’t take into consideration factors such as time of day.
I really appreciate your comments. I am beginning to realize I was probably making this much more difficult than I should have.

 
A good data collection plan and drilldown will take you from the corporate goal or objective that the project supports all the way through to the process, the process output, a specific characterisitic of the output, its operational definition, unit and metric, data type, spec, standard, and DO per unit.
From here, you know precisely what questions need to be answered and thus, what data should be gathered and what tools (generally) you will be using to analyze the data set. 
Good luck. And do the work up front in Define and Measure.  Makes things a lot easier.