Clear visual to explain the signal vs noise

[SJaffa]

https://github.com/UoMResearchIT/programming_with_python/blob/19edfb758c78c9614ae7a2406dfa581a473f47ac/_episodes/03-numpy_essential.md?plain=1#L220

This might not be clear for people who have not done this sort of signal processing. We should explain what is the "signal" - the bump - and what is the "noise" - the data that is around zero but not exactly zero because of the random numbers.

I made this plot to try to visualise the filtering we are doing:

plt.plot(x, noisy)
plt.plot(x, [mean_noisy]*len(x), 'g-')
plt.plot(x, [mean_noisy+stddev_noisy]*len(x), 'r-')
plt.plot(x, [mean_noisy-stddev_noisy]*len(x), 'r-')

[andrewgait]

This could be done; if it is, then I would also suggest that the figure later on (fig/gauss_curve_signal.png) could be modified to have the same x-axis as the original figure, as this would also help in terms of understanding what happens during the filtering.