I am researching on pay-scales, and wish to receive advise to treat data of salaries.
My interest is to approximate the salary corresponding to different hierarchical levels in an organisation.
How to bin
I thought about using quantiles : knowing that there are, say, 10 levels in an organisation (e.g. President, Director, ..., Worker) I would like to estimate the average pay for the corresponding level.
I thought to use quantiles; am looking at the doc of pandas :
Which choice would it be more appropriate ?
My distribution seems to be linear to a certain point, and then it seems to follow an exponential curve:
Could you advice best approach to approximate the quantiles ?
I am in doubt if calculating quantiles against the whole distribution, or if infer two distributions to best reflect observations.
I thought about extracting one distribution as a collection of data points lower than x standard deviations, and a collection of points greater than x standard deviations.
Respectively, I would have these distributions then:
(describing lower payscales, which seems to me linear)
(describing higher payscales, which seems to me exponential)
Advices for the analysis are much appreciated.
Considerations about the sample
Keep in mind each values are reported differently in their frequency (for lower payscales, there are more reports, which it makes sense because there is more turnover).
Should I keep into account to estimate an error ?