I don't understand all the gymnastics to correct for the fact that months end on different days (30, 31, etc.) and all the effort put into dealing with days by number.
For a given month, you can determine the last day. You can also determine the last weekday.
So just do a series that for each month shows "average weekday citations, non automatic" and "final weekday citations, non automatic" (and maybe "first weekday citations, non automatic").
This is exactly my point - the quotas are monthly not daily, but he's doing the wrong kind of work. He's putting his data into a daily format and then trying to extrapolate monthly information.
The right way to solve this problem is to find the exact thing you want - the precise last weekday of the month, the precise first weekday, and a precise weekday average - which is trivial with any decent date/time library. These would be the RIGHT kind of gymnastics. Tallying information by day and then running some kind of regression to approximate last-day traffic is a waste when you can just GET the last-day traffic.
For a given month, you can determine the last day. You can also determine the last weekday.
So just do a series that for each month shows "average weekday citations, non automatic" and "final weekday citations, non automatic" (and maybe "first weekday citations, non automatic").