In [52]:

    

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


    

In [53]:

    

n = 1000
x = np.arange(1,n + 1) + np.random.random(size=n)*500
y = np.arange(1,n + 1) + np.random.random(size=n)*250


    

In [54]:

    

sns.set()

_ = plt.plot(x,y,marker='.',linestyle='none')
_ = plt.xlabel('x')
_ = plt.ylabel('y')

plt.margins(0.02)
plt.show()


    

In [55]:

    

# Pearson correlation coefficient
pearson_correlation_coefficient = np.corrcoef(x,y)[0,1]
print(pearson_correlation_coefficient)


    

    




0.862042994603

In [56]:

    

# linear reg.
slope, intercept = np.polyfit(x,y,1)
x_reg = np.array([min(x),max(x)])
y_reg = slope*x_reg + intercept


    

In [57]:

    

sns.set()

_ = plt.plot(x,y,marker='.',linestyle='none')
_ = plt.plot(x_reg,y_reg,color='red')
_ = plt.xlabel('x')
_ = plt.ylabel('y')

plt.margins(0.02)
plt.show()