import numpy as np
import matplotlib.pyplot as plt
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])
b1, b0 = np.polyfit(x, y, 1)
print(f"b_0 = {b0:.2f}")
print(f"b_1 = {b1:.2f}")
plt.scatter(x, y)
plt.plot(x, b1 * x + b0)
plt.xlabel("x")
plt.ylabel("y")
plt.show()

import numpy as np 
import matplotlib.pyplot as plt  
def estimate_coef(x, y): 
n = np.size(x)  
m_x = np.mean(x) 
m_y = np.mean(y)  
SS_xy = np.sum(y*x) - n*m_y*m_x 
SS_xx = np.sum(x*x) - n*m_x*m_x 
b_1 = SS_xy / SS_xx 
b_0 = m_y - b_1*m_x  
return (b_0, b_1)  
def plot_regression_line(x, y, b):  
plt.scatter(x, y, color = "m", 
marker = "o", s = 30) 
y_pred = b[0] + b[1]*x 
plt.plot(x, y_pred, color = "g")  
plt.xlabel('x') 
plt.ylabel('y') 
plt.show() 
def main(): 
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 
y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12]) 
b = estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \ 
\nb_1 = {}".format(b[0], b[1]))  
# plotting regression line 
plot_regression_line(x, y, b)  
if __name__ == "__main__": 
main()