I would normally use the Distribution platform to fit such models but the negative binomial distribution is not one of the choices. Here I modelled your data with the gamma Poisson distribution, obtained the goodness-of-fit test statistics, and estimated the 95% quantile:
![Screen Shot 2019-04-09 at 7.52.00 AM.png Screen Shot 2019-04-09 at 7.52.00 AM.png](https://community.jmp.com/t5/image/serverpage/image-id/16807i334835426458DD67/image-size/large?v=v2&px=999)
It seems to do well.
The negative binomial and zero-inflated negative binomial distribution models are available in the JMP Pro Generalized Regression platform. I used an intercept-only linear predictor to get these results:
![Screen Shot 2019-04-09 at 8.01.12 AM.png Screen Shot 2019-04-09 at 8.01.12 AM.png](https://community.jmp.com/t5/image/serverpage/image-id/16808iE544429D7693BC95/image-size/large?v=v2&px=999)
![Screen Shot 2019-04-09 at 8.02.51 AM.png Screen Shot 2019-04-09 at 8.02.51 AM.png](https://community.jmp.com/t5/image/serverpage/image-id/16809iB1339DB027B2D4EA/image-size/large?v=v2&px=999)
The AICc suggests that the gamma Poisson distribution is one of the best fits to the data.