Pierre GM wrote: > On Dec 18, 2009, at 10:34 PM, Andrew Straw wrote: > >> Fernando Perez wrote: >> >>> On Fri, Dec 18, 2009 at 2:28 PM, Andrew Straw <straw...@astraw.com> wrote: >>> >>> >>>> (This still leaves open the question of what the notches actually _are_...) >>>> >>>> >>> No idea. I'd still leave the code instead written as >>> >>> notch_max = med + (iq/2) * (pi/np.sqrt(row)) >>> >>> >> Further searching turned this up: >> http://seismo.berkeley.edu/~kirchner/eps_120/Toolkits/Toolkit_01.pdf >> >> It says that >> >> median +/- 1.57 * (iq / sqrt(n)) is the median, plus or minus its standard >> error. >> >> >> I can't find any further support for this notion, though. >> > > > Looks like the std error of the median is (1.253*std error of the > mean=1.253*std dev/sqrt(nb of obs)). > The 1.57 looks like it's 1.253^2, but I wouldn't bet anything on it... > > Also, I think that formula is only for normally distributed data. Which, especially if you're using boxplots, medians, and quartiles, may not be a valid assumption.
Maybe we should at least raise a warning when someone uses notch=1. The current implementation seems dubious, at best, IMO. -Andrew ------------------------------------------------------------------------------ This SF.Net email is sponsored by the Verizon Developer Community Take advantage of Verizon's best-in-class app development support A streamlined, 14 day to market process makes app distribution fast and easy Join now and get one step closer to millions of Verizon customers http://p.sf.net/sfu/verizon-dev2dev _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
