My first thread on the board. It is my hope that I am posting this to the right thread

I was wondering if you could help me with a data analysis problem. I am still learning my ways around statistics. Here is a description of the data and of the issue and I am attaching couple of descriptive diagrams.

I have over 150K incident ticket information and I have done the required descriptive statistics. The data distribution is not normal and probably, I would need to use nonparametric statistics. Poisson distribution might be one of the solution but I am having hard time figuring how to use it in SAS. I also would like to investigate Kruskal Wallis and Weibull. Does somebody have SAS code to do distribution fit to check which distribution will work for my data.

My dependent variable is the duration or resolution time (time between ticket start and resolved times)

The data is not normal and I am seeing many outliers. I have thousands of outliers where it seems like the ticket was never closed and is definitely skewing my data. I have read enough about outlier detection and deletion but I am not sure if I can delete any of them.

I will appreciate any help regarding how to deal with outliers especially the ones like these ones dealing with help desk tickets.

The mean for the ticket resolution time or duration is over 10 days because of few thousand outliers some as high as 1000 days while the median is less than one day (0.90). Standard deviation is 56. Skewness of the resolution time is 7 with kurtosis of 62. 95% quantile is 61 days while 100% MAX quantile is 1073 days. This shows the effect of outliers.

I have many other ticket categories such as tier, responsible team, the ticket problem etc.

After I complete the statistical analysis, my goal is to model the results for process improvement research.

Thanks in advance for all your help.