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]:
In [3]:
incomes.var()
Out[3]:
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?
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