It actually improves a bit with each point you remove showing that the relation ship is really a curve.
I'm not sure about this, especially the point for concentration 400 is a little bit to high for a perfect fit for the proposed polynom equation.
I have tried to extract the raw data from your plot resulting in a quite similar equation for the linear regression.
Code: Select all
10 79928,9520426
30 215808,170515
50 343694,493783
100 687388,987567
150 1015097,69094
200 1342806,39432
250 1662522,20249
300 1966252,22025
400 2629662,5222
600 3732682,06039
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Von x = 1,000000000000000e+01 bis x = 6,000000000000000e+02
B (y-intercept) = 5,818973365175724e+04 +/- 2,347552364405168e+04
A (slope) = 6,265046104296665e+03 +/- 8,580634161502927e+01
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Chi^2/doF = 2,294888771781772e+09
R^2 = 0,998501595806494
When I remove the data for the highest concentration (with 1.4 AU peak-height?) I see a significant better R2:
Code: Select all
Von x = 1,000000000000000e+01 bis x = 4,000000000000000e+02
B (y-intercept) = 2,471072059394117e+04 +/- 6,284867809998377e+03
A (slope) = 6,524003455545055e+03 +/- 3,024975703242817e+01
--------------------------------------------------------------------------------------
Chi^2/doF = 1,297741125094361e+08
R^2 = 0,999849530629616
Without the datapoint for conc. 600 your data fits good for linear regression and y-intercept is lower.
R² seems also be good when we force the regression through zero using a function without y-intercept
Code: Select all
Von x = 1,000000000e+01 bis x = 4,000000000e+02
A = 6,618775589e+03 +/- 3,062300097e+01
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Chi^2/doF = 3,643229407e+08
R^2 = 0,999517232
As Tom recommended above I would also suggest you a closer look on the variability within the respective level. Maybe it will be also a good idea to see the correlation for peak-heights instead of area.
Regards
Klaus