[skj]

It screams exponential at me, especially given a potential underlying model where every sick person has a .x probability of getting every individual they work with sick. As the number of individuals goes up with no change in the rate of sickness from outside the office, the number of sick people should go up exponentially (as with any multiplicative process).

Edit: actually I think I completely misinterpreted the data. Now that I look more closely, I have no idea what the X axis is for. I assumed it was number of employees in a company whose sick time was somehow represented by bar height, but is it just a list of all employees sorted by how much sick time was taken?

If so, this is probably an example of a normal distribution with an exponential tale.

[JonathonW]

I'm pretty sure it's just a list of employees sorted by how much sick time is taken, so the X-axis is an "employee index number".

More interesting (and pertinent when trying to find a pattern in this data) would be a histogram for sick time taken. Trying to fit a curve to the graph as-is isn't useful, because the X-axis doesn't represent anything meaningful.

[phrixus]

This is my thought as well. So you fit a curve to a sorted list of each employees sick time. Does this give you any additional insight? So it follows a log function. Does that mean anything?

If you do a histogram and fit a function you get something that could conceivably be interpreted as a probability distribution function, you might be able to say something about predicting the sick time a given employee will take and the uncertainty of your prediction.

But I honestly don't see what visualizing the data in the method of the post, or fitting a function to it contributes. Hope that doesn't violate the new no negativity policy of HN.

[skj]

tale = tail. oy.