Hello miner,

Thank you for your reply. Indeed, I understood today there are other distributions that I can apply like weibull and log-normal.

My question is related to another post of me on ppm calculations. Though I got there an alternative, I still need to challenge what is currently created as a model. In my view they didn't take in account this distribution. I will explain and try to keep it simple.

We have x amount of complaints per month

We have x sales per month

If there would be no delay between when product is being sold and the complaint comes in we could just calculate the fraction

(complaints / sales) per month.

However, there is a delay between time of sales and registration of complaint.

Of the total complaints we have a subset of which the serial numbers are known.

With that we know the delay time between sales and complaint.

If I look at the how the distribution of delay looks like it is like (I thought) a half normal distribution, but the others are better because a complaint will not be raised with no delay.

The idea is to use the right distribution, and take the mode of the fitted model as correction for delay to use on all complaints to put them in the "right" month of sales.

However this introduces the spread of this distribution and I understood today that the month of sales is a condition used on this fraction calculation and therefore if I correct with the mode of this distribution the fraction calculated would get a spread introduced.

Final goal is to take that into account, draw the fitted line of the fraction model with the introduced spread and proof that this spread is so big that it makes no sense to use this model (assumption is that it is)