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

You can execute this code directly, this will find you the polynomial, and will find you the R-value you can put a comment down below if you need more explanation.

from scipy.stats import linregress
import numpy as np

x = np.array([1,2,3,4,5,6])
y = np.array([2,3,5,6,7,8])

p3 = np.polyfit(x,y,3) # 3rd degree polynomial, you can change it to any degree you want
xp = np.linspace(1,6,6)  # 6 means the length of the line
poly_arr = np.polyval(p3,xp)

poly_list = [round(num, 3) for num in list(poly_arr)]
slope, intercept, r_value, p_value, std_err = linregress(x, poly_list)
print(r_value**2)