54 lines
1.2 KiB
Python
54 lines
1.2 KiB
Python
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
def estimate_coef(x, y):
|
|
# number of observations/points
|
|
n = np.size(x)
|
|
|
|
# mean of x and y vector
|
|
m_x, m_y = np.mean(x), np.mean(y)
|
|
|
|
# calculating cross-deviation and deviation about x
|
|
SS_xy = np.sum(y*x - n*m_y*m_x)
|
|
SS_xx = np.sum(x*x - n*m_x*m_x)
|
|
|
|
# calculating regression coefficients
|
|
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):
|
|
# plotting the actual points as scatter plot
|
|
plt.scatter(x, y, color = "m",
|
|
marker = "o", s = 30)
|
|
|
|
# predicted response vector
|
|
y_pred = b[0] + b[1]*x
|
|
|
|
# plotting the regression line
|
|
plt.plot(x, y_pred, color = "g")
|
|
|
|
# putting labels
|
|
plt.xlabel('x')
|
|
plt.ylabel('y')
|
|
|
|
# function to show plot
|
|
plt.show()
|
|
|
|
def main():
|
|
# observations
|
|
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])
|
|
|
|
# estimating coefficients
|
|
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()
|