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I am trying to find the best function to fit my data. I have an image where items fall inside a circular region. My x values represent the radius of a circle, as x increases, the number of items (y values) that fall into it increases. So, y values are accumulated until all of the items are included. After normalization, y always ends at 1.
A sample data looks like this:
What function should I use to get the best fit? Initially, I thought Poisson. Here But it doesn't seem correct.
I proposed the function below. The explanation of the calculus with the OP's data is given as a previous answer in : https://stackoverflow.com/questions/66110390/fitting-commutative-poisson-distribution-in-python-doesnt-seem-correct?noredirect=1#comment116891714_66110390
Another and even simpler function :
