[EMAIL PROTECTED] wrote: > Send relax-users mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.gna.org/listinfo/relax-users > or, via email, send a message with subject or body 'help' to > [EMAIL PROTECTED] > > You can reach the person managing the list at > [EMAIL PROTECTED] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of relax-users digest..." > > > Today's Topics: > > 1. Error propagation for duplicates, triplicates, > quadriplicates... (Sebastien Morin) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 03 Sep 2007 16:38:40 -0400 > From: Sebastien Morin <[EMAIL PROTECTED]> > Subject: Error propagation for duplicates, triplicates, > quadriplicates... > To: [email protected] > Message-ID: <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=ISO-8859-1 > > Hi ! > > I recorded 4 sets of R1 and would like to use them all and, so, extract > a mean value and also an associated error... > > I would like to get the opinion of someone maybe more used with > statistics than me... > > I thought about : > > 1. calculating the mean error > 2. calculating the standard error (should be the best way, no) > 3. calculating the standard deviation > 4. extracting an error by calculating the extremes the value can reach > in every dataset based on the error of each dataset > > What would the best error to use in a statistical point of view, but > also in a model-free point of view..? > > Also, is there a way to use both the errors in the datasets and a error > extrated for the observed deviation of data..? > > Note that the errors from each datasets were calculated directly from > the fits, here using the 'autoFit.tcl' script from NMRPipe with data > processed as Gaussian lines. > > Also, in the case of duplicates or triplicates, should one use the same > approcah ? > > Thanks ! > > > S?b :) > > There are several ways forward here that are less obvious
1. fit all the data together... even if you have points at duplicate times these will still add to the fitting (though note that if the data wern't all measured under the same conditions (i.e. the signal to noise differs) you will have to use a weighted least squares procedure) 2. add all the intensities at the same timepoints together (having wieghted by the noise intensity) this will give the traditional root 2 increase in s/n each time you double the number of points both of these are easy and avoid the 'cominatorial statistics' problem (though of course they may not be the best methods (though I would do 1 it should be good)) regards gary -- ------------------------------------------------------------------- Dr Gary Thompson Astbury Centre for Structural Molecular Biology, University of Leeds, Astbury Building, Leeds, LS2 9JT, West-Yorkshire, UK Tel. +44-113-3433024 email: [EMAIL PROTECTED] Fax +44-113-2331407 ------------------------------------------------------------------- _______________________________________________ relax (http://nmr-relax.com) This is the relax-users mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-users
