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Subject: Re: Using Fit to interpolate data

  • To: mathgroup at smc.vnet.net
  • Subject: [mg127475] Subject: Re: Using Fit to interpolate data
  • From: "McHale, Paul" <Paul.McHale at excelitas.com>
  • Date: Sat, 28 Jul 2012 02:38:19 -0400 (EDT)
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I like Bill Rowe's:

In[12]:= params=FindFit[fiberDataDensitiesFeierabend, a Exp[b
x], {a, b}, x]

Out[12]= {a->2.06978*10^6,b->-1.61827}

and

Plot[a Exp[b x] /. params, {x, 7, 16.5},
  Epilog -> {PointSize[.02], Point[fiberDataDensitiesFeierabend]}]



Here is another alternative of the same thing.

In[]:= f = NonlinearModelFit[fiberDataDensitiesFeierabend, a Exp[b x], {a, b}, x]
Out[]:= FittedModel[2.06978x10^6 * e^(-1.61827 * x)   ]

In[]:= Plot[f[x],{x,7,16.5},Epilog->{PointSize[.02],Point[fiberDataDensitiesFeierabend]}]
Out[]:= (same result)

In[]:= f["FitResiduals"]
Out[]:=   {-0.0000117777,0.0474942,0.0884309,0.128468,-0.038007,0.00348739}

In[]:= f["ParameterConfidenceIntervals"]
Out[]:= {{1.11329*10^6,3.02627*10^6},{-1.68272,-1.55381}}


I really like the FitResiduals you can use with object returned by NonlinearModelFit[].  There is also EstimatedVariance, BestFit.  Just saying, you might want to look into it.  There is an excellent youtube video.

http://www.youtube.com/watch?v=KolZZm8If9Q




Paul McHale  |  Electrical Engineer, Energetics Systems  |  Excelitas Technologies Corp.
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