Scott,
On Thu, May 12, 2011 at 10:51 AM, Scott Calvin
dr.scott.cal...@gmail.com wrote:
Hi Brandon,
Matt and Bruce both gave good, thorough answers to your questions this
morning. Nevertheless, I'm going to chime in too, because there are some
aspects of this issue I'd like to put emphasis on.
Matt,On May 13, 2011, at 8:39 AM, Matt Newville wrote:After all, the epsilon shouldbe different for differentk-ranges, as your signal to noise ratio probably changes as a function of k.Using the same epsilon doesn't reflect that.Without seeing the data in question, this seems like speculation
Hi Scott,
Sorry, I read epsilon as noise in chi(k). This is the most
meaningful physical/statistical measure: epsilon_r surely depends on
k-weight and can depend on k-range as it samples different portions of
the spectra. Like you say, it will tend to increase as you increase
the k-range.
On
Hi Brandon,
Matt and Bruce both gave good, thorough answers to your questions this
morning. Nevertheless, I'm going to chime in too, because there are
some aspects of this issue I'd like to put emphasis on.
On May 11, 2011, at 8:46 PM, Brandon Reese wrote:
I tried your suggestion with
Thanks again for everyone's very informative and thorough replies, this
mailing list is great!
Bruce, I hope that I didn't convey that reduced chi-square (RCS) wasn't
useful. I am constantly using it when figuring out how to appropriately
model my data. My comment stemmed from what you said
Hi Brandon,
I don't find this terribly surprising.
First, a little background which you may or may not know:
Reduced chi-square is a statistical parameter which requires a
knowledge of the uncertainty of the measurement to compute. In theory,
therefore, it knows that a good fit to noisy
