1D Polynomial Curve fitting in Numpy and Matplotlib

This tutorial is meant to provide a quick intro to a couple useful subjects: generating polynomial data, introducing noise to that data, creating a fit with the least squares method, and graphing the fit and data together with an R2 value. First, we are going to make some numpy arrays representing x and y coordinates for a line:

import numpy as np
x = np.linspace(0, 50, num=50)
y = np.linspace(0, 30, num=50)

Here x is an array that has 50 …

arrays data intro least matplotlib noise numpy polynomial squares together tutorial value