On Tue, 2015-06-09 at 14:36 +0000, Carrico, Paul wrote: > Dear all > > > > I’m currently thinking in a way to compare experimental have a bell > shape, composed of points and not connected to any parametric curve > (the bell shape assumption could answer to the need in a first step). > > > > A way I can imagine is to calculate a kind of “ bandwidth” (-3 db). > > > > I add a look in the Scilab help but nothing obvious appears > > > > what is the best way to proceed to: > > - calculate/determine the intersection points with the > “pseudo”-curves, > > - the number of points may change and may have different abscissa ? > > - the points could not necessarily by expressed by a parametric curve > (to fit on before calculating the intersection), > > > > Any suggestion are welcome > > > > > > NB: please find hereafter a basic example using a gauss curve (just to > illustrate the purpose) > To the extent that you can do so without giving away any proprietary information, could you please tell us what you're really trying to do?
Citing a Gaussian distribution (a "bell curve") implies that you're trying to find a probability density, but citing a "bandwidth" implies that you're trying to characterize some sort of a signal processing system. Those two concepts don't exactly fit well together when you're talking of one set of data. If you're taking a bunch of measurements and you're assuming a Gaussian distribution of errors, and you want to estimate the mean and variance of the distribution, then you want to use mean(x) and var(x). This is kind of the quick & dirty way to do things, but with a large enough sample it's not going to be very far off the mark (if you have less than a couple of dozen samples you may want to investigate the Student t distribution, but I don't like recommending it if you don't have a solid background in statistics). -- Tim Wescott www.wescottdesign.com Control & Communications systems, circuit & software design. Phone: 503.631.7815 Cell: 503.349.8432 _______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users