Stats noob here. I've been trying to find out if there is a technique to perform a certain type of analysis but so far a lot of searching hasn't yielded anything.
My question is, if a large peak or trough is identified in an organisation's overall rate, is there a way of identifying which of the organisation's production sites has contributed the most to this rate change?
The data describes an organisation and it's performance against a rate based performance measure (let's say % wastage on a washer production line). The dataset contains 4 fields. These fields are:
- Site (There are 100+ of these)
- Numerator (Number of washers lost to wastage)
- Denominator (Total number of washers produced)
The organisation monitors it's performance based on its overall rate (sum of all numerators / sum of all denominators) by month. This gives a trend line. If there is a sudden large increase/decrease in rate then the organisation wants to target the sites which have most contributed to this rate change.
I'm not certain if there is an answer to this question, as one site could see a decrease in denominator and another could see an increase in its numerator value, meaning these sites both show a small change, whilst the organisation as a whole may see a larger scale change when the figures are combined and the overall rate is calculated.
I was originally thinking about something like a logistic regression with weighting but wasn't sure if this was quite the way to go...