Regression.py

from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use('fivethirtyeight')
xs=np.array([1,2,3,4,5,6],dtype=np.float64)
ys=np.array([5,4,6,5,6,7],dtype=np.float64)

def best_fit_slope_and_intercept(xs,ys):
    m=(((mean(xs)*mean(ys)) -mean(xs*ys)) /
        ((mean(xs)*mean(xs))-mean(xs*xs)))
    b=mean(ys)-m*mean(xs)
    return m,b

m,b=best_fit_slope_and_intercept(xs,ys)
regression_line=[(m*x)+b for x in xs]

predict_x=8
predict_y=(m*predict_x)+b


plt.scatter(xs,ys)
plt.scatter(predict_x,predict_y,color='g')
plt.plot(xs,regression_line)
plt.show()
#for x in xs:
#    regression
print(m,b)
plt.plot(xs,ys)
plt.show()