How do I calculate r-squared using Python and Numpy?

Question

I\'m using Python and Numpy to calculate a best fit polynomial of arbitrary degree. I pass a list of x values, y values, and the degree of the polynomial I want to fit (lin

Answer 1

From scipy.stats.linregress source. They use the average sum of squares method.

import numpy as np

x = np.array(x)
y = np.array(y)

# average sum of squares:
ssxm, ssxym, ssyxm, ssym = np.cov(x, y, bias=1).flat

r_num = ssxym
r_den = np.sqrt(ssxm * ssym)
r = r_num / r_den

if r_den == 0.0:
    r = 0.0
else:
    r = r_num / r_den

    if r > 1.0:
        r = 1.0
    elif r < -1.0:
        r = -1.0