In essense, for the gene in question, each horse is either AA, CA or CC. (As usual, the foal gets one gene from each parent) Several houndred horses have been tested so there are a lot of observations. 30 foals (children) of one stallion (male horse) has been tested, and 10 are CA while 20 are CC. Since that stallion has CC foals I know he must be CA or CC. Since there are so many CC foals, that seems most likely. Unfortunately we don't know the genes of the mothers, but the population is 11 % AA, 20 % CC and 69 % CA so I guess we can just assume the 30 mothers are distributed according to this.

So how do I calculate the posterior probability that this stallion is CC? I suspect I have to use Bayes' Theorem in one form or another but I will reluctantly admit that I am not sure how to apply it and how to define the terms correctly. The strange part is that at some point I was actually relatively comfortable with this, but I have forgotten quite a bit as I never use this part. So any and all input is greatly appreciated! ]]>

Im not a statistitian, although I have some reasonable education in mathematics.

This isn't a homework question (I'm 37 lol), its just something I have been working on as a personal project lately.

The following graph represents the % payout of an online video slot game. I have collected data points comprising of 25 spins on this slot, recording the return to player (RTP) of each group of spins. This has then been charted, grouping into 10% bands using a pivottable. There are 150 data points forming this data set.

As you can see, it has begun to resemble some kind of probability distribution. I was thinking initially a Poisson distribution. However, my attempts to map a true Poisson curve to this data have failed.

So I would like to request some help to:

a) find out which type of probability distribution this resembles; and

b) help me estimate the parameters that will enable me to plot the theoretical probability curve onto this experimental data set.

To my mind, the curve I need will not be symmetrical. As you can see from the chart, there is a significant one sided tail to the distribution. There is no tail on the left hand side because obviously there is a hard threshold at zero RTP.

Any help appreciated.

Thanks

Dan ]]>