Standard Deviation and Variance


In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

incomes = np.random.normal(100.0, 50.0, 10000)

plt.hist(incomes, 50)
plt.show()



In [2]:
incomes.std()


Out[2]:
49.794255862554017

In [3]:
incomes.var()


Out[3]:
2479.4679169054948

Activity

Experiment with different parameters on the normal function, and see what effect it has on the shape of the distribution. How does that new shape relate to the standard deviation and variance?


In [ ]: