Bit of a noob in this stats world, so apologies in advance for any naiveté. I did a fair bit of stats long ago in college but it's a distant memory, so please assume little knowledge!
The dataset I've got is of the amount of money spent by medical practices in a given month. There already exists a formula that weighs the population (produced long ago using different data) to account for ages (old patients cost more, 20-somethings less etc.), however there are lots of other factors, that we have data for, it isn't weighing. I'd like to try and do that.
So my question is: what approach should I take to improve the population weighting to account for these other factors, for which there is a correlation.
Ideally I'd like to generate a formula such as:
Improved weighted population = Existing weighted population * (x * %long term condition) * (y * population deprivation levels)...
The aim would be to improve the error between the current prediction (spend__per_astroPU) and the observed true result (spend_per_raw_head).
I've put a sample of the dataset below, and a link to a python notebook with the full dataset imported into dataframes.
drug_spend Total Population astroPU_weight ASTROPU2013_Population spend_per_raw_head spend__per_astroPU % with a long-standing health condition 0 804443.050 4150 1.146 4757 193.842 169.107 58.230 1 17534209.330 19886 1.130 22478 881.736 780.061 60.218 2 3593560.340 9471 1.033 9786 379.428 367.214 61.756 3 3043412.970 7929 1.272 10089 383.833 301.657 57.046 4 11163851.800 13733 1.033 14189 812.922 786.796 54.217