linearregression.py

import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig

x = np.linspace(1,100,200)

error = (np.random.normal(size=200))*1000 #generate random errror centered at 0
  
y =  20*x + 100 + error #example data with slope, intercept and random error
#y =  x**2 +20*x + 100 + error

plt.figure()
plt.plot(x,y,".")
plt.show()


x_m = x.mean()
y_m = y.mean()

x_distances = x - x_m
y_distances = y - y_m

numer = np.sum(x_distances*y_distances)
denom = np.sum(x_distances**2)

slope = numer/denom

intercept = y_m - slope*x_m

plt.figure()
plt.plot(x,y,".", x, intercept+slope*x, "-")
plt.show()

"""
errors = y - (intercept+slope*x)
plt.figure()
plt.plot(x,errors,"r.")
plt.show()
"""