How to plot a best fit curve to my data points?

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Nisar Ahmed
Nisar Ahmed on 2 Jun 2022
Commented: Sam Chak on 3 Jun 2022
Hi,
I have attached a data (inverted_qp.mat Inv_qpAVO freq;) and a plot between fre and Inv_qpAVO is shown below in red color.
circled points on left hand side are the issue and I want to a best-fit curves passes through these points just like shown on the right hand side figure (in blue color a solid line).
The best fit curve should start from the fist point to the end point as given a solid blue line. Thanks
  7 Comments
Sam Chak
Sam Chak on 3 Jun 2022
@Image Analyst, thanks for the insights into the analysis of the data that provides a basis for understanding the curve fitting. 👍

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Answers (2)

Kevin Holly
Kevin Holly on 2 Jun 2022
If you have the Curve Fitting Toolbox, you can use the Curve Fitting app after you import your data into the workspace. Within the app, there are many curve fits you could select from. Here is a video showing the process.
  1 Comment
John D'Errico
John D'Errico on 3 Jun 2022
<Moved to a comment from @Nisar Ahmed
@Kevin Holly I tried all the options Curve Fitting Toolbox have but not got best fit results

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Nisar Ahmed
Nisar Ahmed on 3 Jun 2022
@Alex Sha thank you for you answer,
I am sorry I did not understand, do you mean, I need to compute
f(x) = p1 + p2.*x + p3*x.^1.5 + p4.*log(x) + p5.*exp(p6.*x)
while using
p1 = -0.137921750658333;
p2 = 4.83156544429652E-5;
p3 = -3.87422699831525E-7;
p4 = 0.000904269701487609;
p5 = 0.138139976684668;
p6 = -0.000327312583052385;
and x = Inv_qpAVO;
I am trying as I wrote above and having this error
Array indices must be positive integers or logical values.
Error in InQP_f (line 29)
f(x) = p1 + p2.*x + p3*x.^1.5 + p4.*log(x) + p5.*exp(p6.*x)
  1 Comment
Torsten
Torsten on 3 Jun 2022
p(1) = -0.137921750658333;
p(2) = 4.83156544429652E-5;
p(3) = -3.87422699831525E-7;
p(4) = 0.000904269701487609;
p(5) = 0.138139976684668;
p(6) = -0.000327312583052385;
f = @(x)p(1) + p(2)*x + p(3)*x.^1.5 + p(4).*log(x) + p(5).*exp(p(6).*x);
plot(x,f(x))

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