You can sort all kinds of things that follow an underlying probability distribution in order to find some kind of function to describe the behavior. That's not something I'd consider new (or interesting).
What is more interesting, though, is what kind of probability distribution it follows. From that, it is straightforward to figure out other representations (as the sorted plot by the author). But it's kinda annoying to figure that out from that plot in reverse since I'd need to calculate cumulative distribution functions.
From what other's have posted the log-function fit means the probability distribution is exponential (since the cumulative distribution function is exponential). I wouldn't know, maybe a Gaussian is a better fit, exponential seems unnatural, but I'd need to sit down and compute error functions in order to figure that out from that plot.
It would've been great if the author would have made the effort to exclude the obvious guess that the sickness probability distribution function is a Gaussian distribution.
What is more interesting, though, is what kind of probability distribution it follows. From that, it is straightforward to figure out other representations (as the sorted plot by the author). But it's kinda annoying to figure that out from that plot in reverse since I'd need to calculate cumulative distribution functions.
From what other's have posted the log-function fit means the probability distribution is exponential (since the cumulative distribution function is exponential). I wouldn't know, maybe a Gaussian is a better fit, exponential seems unnatural, but I'd need to sit down and compute error functions in order to figure that out from that plot.
It would've been great if the author would have made the effort to exclude the obvious guess that the sickness probability distribution function is a Gaussian distribution.