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I've done a NonLinearRegression with this data

{{0.245, 0.0917011}, {0.25, 0.0894304}, {0.25, 0.108895}, 
 {0.25, 0.111991}, {0.251, 0.0898849}, {0.251, 0.107753},
 {0.254, 0.114524}, {0.254, 0.121882}, {0.255, 0.130478},
 {0.255, 0.186865}, {0.256, 0.108817}, {0.256, 0.128742},
 {0.256, 0.131999}, {0.257, 0.150797}}

The model is a simply parabola:

y=a x^2+b x +c

The coefficiets are:

 a= 317.459
 c= 18.9415

Then I've done the same with Excel and the coefficients are the same, but R^2 is not the same. In Mathematica I've got R^2=0.97 and Excel tells me R^2=0.45. Why there is this incoherence?

share|improve this question
Maybe this will be useful. – b.gatessucks Aug 14 '13 at 8:54
Plus, correct me if I am wrong, but you will get different values of R depending on the data - whether it is a sample or the whole population. – Sektor Aug 14 '13 at 8:58
We had an earlier question on this, but I can't find it anymore. I believe the answer was that in a non linear model fit rsquared has a different meaning and that LinearModelFit should be used in this case. – Sjoerd C. de Vries Aug 14 '13 at 14:34
@b.gatessucks Your link does not work for me. Perhaps it depends on being logged in into google groups? – Sjoerd C. de Vries Aug 14 '13 at 14:36
@b.gatessucks Your link is very useful. Thanks a lot – Mary Aug 14 '13 at 15:38

lm=LinearModelFit[data,{1,x,x^2},x] yields the same coefficient estimates and the R^2 for this model coincides with Excel, i.e. Excel uses linear regression model



share|improve this answer
thanks I agree with your comment. In fact I've done a linear regression no a non Linear model. – Mary Aug 14 '13 at 15:36

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