![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/288441/image.png)
How do I find curve of best fit or create one manually to fit?
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angle = [45,50,55,60,65,70,75,80,85]';
P = [55.51, 69.5, 78.07, 82.06, 81.81, 77.3, 68.12, 53.42, 31.59]';
scatter(angle, P, 'bx');
%Also I apparently don't have the cftool so I can't use that I'm afraid.
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Ameer Hamza
2020 年 4 月 28 日
編集済み: Ameer Hamza
2020 年 4 月 28 日
It looks like a parabols. If you have optimization toolbox, you can use lsqcurvefit to fit this equation (y=a*x^2+b*x+c) to the dataset.
angle = [45,50,55,60,65,70,75,80,85]';
P = [55.51, 69.5, 78.07, 82.06, 81.81, 77.3, 68.12, 53.42, 31.59]';
fun = @(a,b,c,angles) a*angles.^2 + b.*angles + c;
param_sol = lsqcurvefit(@(param, angles) fun(param(1),param(2),param(3),angles), rand(1,3), angle, P);
a_sol = param_sol(1);
b_sol = param_sol(2);
c_sol = param_sol(3);
plot(angle, P, 'bx', angle, fun(a_sol, b_sol, c_sol, angle), 'r-');
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/288441/image.png)
You can also do it without any toolbox. Following also fit a parabolic equation of form (y=a*x^2+b*x+c)
angle = [45,50,55,60,65,70,75,80,85]';
P = [55.51, 69.5, 78.07, 82.06, 81.81, 77.3, 68.12, 53.42, 31.59]';
X = [angle(:).^2 angle(:) ones(size(angle(:)))];
params = X\P(:);
P_estimated = X*params;
plot(angle, P, 'bx', angle, P_estimated, 'r-')
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