In [61]:

    

import numpy as np
from matplotlib import pyplot as plt

data_points = np.array([
    [ 4936.490,     0],
    [ 7264.623,   0.7],
    [ 9005.072,  10.4],
    [10343.531,  37.0],
    [11441.591,  77.4],
    [12391.082, 122.8],
    [13245.908, 169.5],
    [14038.782, 216.0],
    [14727.344, 254.0],
    [15398.375, 286.0],
    [15997.332, 314.0],
    [16597.289, 336.0]
])

x, y = data_points.T
plt.scatter(x,y)


    

    
Out[61]:





<matplotlib.collections.PathCollection at 0x7f74250c5dd8>






    

In [62]:

    

a = ( np.mean(y)*sum([i*i for i in x]) - np.mean(x)*sum(np.multiply(x, y)) ) / (sum([i*i for i in x]) - len(x)*np.mean(x)**2)
b = ( sum(np.multiply(x, y)) - len(x)*np.mean(x)*np.mean(y) ) / (sum([i*i for i in x]) - len(x)*np.mean(x)**2)

line = np.polyfit(x, y, 1)
print(line, (a,b))


    

    




[ 3.29418354e-02 -2.47127369e+02] (-247.12736927443734, 0.03294183543689739)

In [68]:

    

def lineeqn(xo):
    return line[0]*xo + line[1]

plt.plot(x, y, 'b+')
plt.plot(x, lineeqn(x), 'r')
plt.grid()
plt.show()


    

In [64]:

    

vsd = [j - line[1] for j in y]

stefs = []
for l in range(len(vsd)):
    stefs.append(2.43*10**(-4)*vsd[l]/x[l])

stefs, np.mean(stefs)


    

    
Out[64]:





([1.2164908818550878e-05,
  8.289769576988127e-06,
  6.949322641028103e-06,
  6.674988525068298e-06,
  6.892411268125929e-06,
  7.254600585621838e-06,
  7.643149169818199e-06,
  8.016361443157123e-06,
  8.26856157727342e-06,
  8.413222222064876e-06,
  8.52354322168773e-06,
  8.53753590322421e-06],
 8.135697912717395e-06)

In [58]:

    

stef = 2.43*10**(-4)*(216.0-line[1])/14038.782*10**-3
stef


    

    
Out[58]:





7.090159134680325e-09