Two uncorrelated random variables are not necessarily independent#
We have seen that if two random variables \(X\) and \(Y\) are independent, then their covariance is zero,
and therefore their correlation coefficient is also zero:
Does the reverse hold? Namely, if you find that the correlation between two random variables is zero, does this imply that they are independent? The answer to this question is a loud NO. We will show that it does not hold through a counter example.
Take these two independent random variables:
and
Then define this new random variable \(Y\) by:
Since there is a functional relationship between \(X\) and \(Y\), they are obviously not independent. But let’s generate some data from them and estimate the correlation:
Show code cell source
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(rc={"figure.dpi":100, 'savefig.dpi':300})
sns.set_context('notebook')
sns.set_style("ticks")
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina', 'svg')
import numpy as np
import scipy.stats as st
xdata = np.random.randn(10000)
zdata = np.random.randn(10000)
ydata = xdata ** 2 + 0.2 * zdata
It’s instructive to look at the scatter plot:
Show code cell source
fig, ax = plt.subplots()
ax.scatter(xdata, ydata)
ax.set_xlabel('$x$')
ax.set_ylabel('$y$');
Well, it’s obvious that they are not independent. Let’s see what the correlation coefficient is:
rho = np.corrcoef(xdata, ydata)
print('rho(X, Y) = {0:1.2f}'.format(rho[0, 1]))
rho(X, Y) = 0.03
Very close to zero. So, \(X\) and \(Y\) are uncorrelated… Rememeber this please! Do the scatter plots and use your common sense. Do not just rely on a number to make decisions.
After you see the scatter plot like this, you get suspicous. You start thinking that there may be a correlation between the square of \(X\) and \(Y\). Let’s estimate the correlation of \(X^2\) and \(Y\) to see what it turns out to be:
rho = np.corrcoef(xdata ** 2, ydata)
print('rho(X^2, Y) = {0:1.2f}'.format(rho[0, 1]))
rho(X^2, Y) = 0.99
Almost one! (It is actually exactly one).