On Wed, Feb 17, 2010 at 9:24 AM, inode0 <inode0@xxxxxxxxx> wrote: > It could but does it and to what extent? Is it a good metric for the > number of contributors joining the project? What does it look like if > we overlay the recent stagnant download trend with the recent > statistics showing numbers of contributors over the same period? Be very careful here. What you really want to do is look for a characteristic cross correlation time...essentially slide the trending graphs of the download metric and contributor metric across each other looking for the best time offset that maximizes how the graphs correlate. I believe numpy(or is it scipy I'll have to look) has a cross correlation function built in. Get me two years of data binned weekly for both download and contributor metrics and I can do the correlation analysis. And to convince myself that the correlation is real I would want to divide the available data set into multiple time chunks and calculate the correlation time in each. If the characteristic correlation time across those chunks are tightly grouped with the overall correlation time calculated from the entire data set then is probably a real correlation between downloads and contributors. Even if the characteristic time has been slowly varying that would also be worth looking into understanding. Until a see a correlation analysis of this type done I've no rationale reason to start looking for a cause/effect relationship. -jef _______________________________________________ advisory-board mailing list advisory-board@xxxxxxxxxxxxxxxxxxxxxxx https://admin.fedoraproject.org/mailman/listinfo/advisory-board
