regression - How to get the sum of least squares/error from polyfit in one dimension Python -


i want linear regression scatter plot using polyfit, , want residual see how linear regression is. unsure how isn't possible residual output value polyfit since 1 dimensional. code:

p = np.polyfit(lengths, breadths, 1) m = p[0] b = p[1] yfit = np.polyval(p,lengths) newlengths = [] y in lengths:     newlengths.append(y*m+b) ax.plot(lengths, newlengths, '-', color="#2c3e50") 

i saw stackoverflow answer used polyval - unsure of gives me. exact values lengths? should find error finding delta of each element polyval , 'breadth'?

you can use keyword full=true when calling polyfit (see http://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html) least-square error of fit:

coefs, residual, _, _, _ = np.polyfit(lengths, breadths, 1, full=true) 

you can same answer doing:

coefs = np.polyfit(lengths, breadths, 1) yfit = np.polyval(coefs,lengths) residual = np.sum((breadths-yfit)**2) 

or

residual = np.std(breadths-yfit)**2 * len(breadths) 

additionally, if want plot residuals, can do:

coefs = np.polyfit(lengths, breadths, 1) yfit = np.polyval(coefs,lengths) plot(lengths, breadths-yfit) 

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