On 4/17/2010 6:53:19 AM, adiaz wrote:
>Done.
>
>Regards. Alvaro.
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Smoothing data in a fitting session is an heresy. The ksmooth might be applied in Fourier fitting as a decision making. The other unique case I have found is the Carbon 12 data. Each of the Mathcad built-in smoother is very limited in application. Two of them deserve consideration: the Paul W SVG an the ksmooth. The Mathcad Built-in ksmooth is the traditional Gaussian, it works well up until it fails. It is a quick built-in tool but again in the case of the Carbon 12, it fails.
This project is back before square 0:
a Mathcad work sheet w/o a data table.
Your model [or Kristjan model] fits well enough some columns but not all, thus proving the model is insufficient. My linfit model is an "�bauche" but inherently more powerful, flexible and faster. It does not seem to fit all as well. It can be generalised to be a "linfit Cheby", but to early considering the data set. Data are just data but normally they come sorted from collection, though this is not a general rule. In fact, experiments may be carried with poor accuracy and still carry the information. In that case, data aren't then sorted, a conjectural case that Mathcad manages pretty well.
So, reach my � page below Marlett and experiment the two options: sorted and not sorted. I'm willing for an act of faith but only one out of several: Not sorted, both sorted, vx sorted, vy sorted. My other act of faith is that you have only ordinates vs an index, that gives sense to the project for a "Gaussian" model or a "linfit Cheby". Again the data column should be truncated, many values are meaningless.
Hope it helps to reconsider the project.
jmG