The raw data, including the killmail dump, can be found here (25 MB).
You can review previous reports here.
For deeper understanding of the indices used in the report, please have a look at the following explanations.
Mineral Price Index (MPI)
The Mineral Price Index (MPI) shows the price changes in all eight minerals used to produce ships and other items in EVE. The weight of each mineral in the index changes each month is based on the relative trade values of the previous month.
Primary Producer Price Index (PPPI)
The Primary Producer Price Index consists of manufacturing items used for the production of other manufacturing items at the secondary stage. Manufacturing items used for the production of final consumer goods are excluded. The index includes such item groups as ore, moon materials, planetary commodities, sleeper relics, and items used in invention.
Secondary Producer Price Index (SPPI)
The Secondary Producer Price Index contains production materials and other production items that are used in the manufacturing of consumer goods, i.e. goods included in the Consumer Price Index.
Consumer Price Index (CPI)
The Consumer Price Index measures the overall price changes of consumer products. This is not limited to consumables such as fuel, ammunition or PLEX, but also includes assets such as ships, modules, implants and starbase structures. In summary, anything that is not primarily used to produce other goods is included in the index, which contains over 4000 individual items.
You have to understand the mechanics of the null sec cartels and who actually the ISK is directed to. Then look at Noizy Gamer’s past blogs, and it will all become clear.
Thanks for pointing it out. It seems that our data procedure that makes these timeseries did not run properly on that day, so the data for that date is missing. In those cases two things can be done, I can (for transparency) leave it as NA, or do a linear interpolation between the surrounding days to fill the gap. I opted for the latter, but I could fix this if it helps
I assume you meant that you opted for the former having NA values for the missing data. In my graph at each import it creates a huge dip at that date. But that’s no big deal, since I just delete the 15.04.2013 afterwards again and all is good. Minimal additional effort.
Just wanted to make sure you know about that anomaly. And thx for the quick reply.
correct! and after some consideration I decided to fill the gap with the number 509154936421287 (result of a linear interpolation) so this won’t be an issue in the future reports!
