My Statistics skills aren't good enough to provide a solid explanation on the reasons for that - hopefully one of the more seasoned statistics experts can edit my answer (or provide their own and delete mine) to give details on this side-note. Al igual que en el ejemplo anterior, el cdigo crtico se ingresar en MATLAB.
Este segundo ejemplo se centra en la creacin de vectores. You can reduce this correlation by subtracting the mean x-value of your data before fitting. El sistema MATLAB est repleto de ejemplos polinmicos que el usuario puede intentar. One note of caution: The errors of a and b will generally be correlated, which makes them unnecessarily big.
Assuming that the confidence intervals are symmetrically spaced around the fitted values (which in my experience is true in all reasonable cases), you can use the following code: cf_coeff = coeffvalues(cf) Ī_uncert = (cf_confint(2,1) - cf_confint(1,1))/2 ī_uncert = (cf_confint(2,2) - cf_confint(1,2))/2 You can access the fit results with the methods coeffvaluesand confint. The option 'poly1' tells the fit function to perform a linear fit. Note: x and y have to be column vectors for this example to work. If you have the curve fitting toolbox installed, you can use fit to determine the uncertainty of the slope a and the y-intersect b of a linear fit.
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