The Normal CDF

Now that you have a feel for how the Normal PDF looks, let's consider its CDF. Using the samples you generated in the last exercise (in your namespace as samples_std1, samples_std3, and samples_std10), generate and plot the CDFs.

This exercise is part of the course

Statistical Thinking in Python (Part 1)

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Exercise instructions

Use your ecdf() function to generate x and y values for CDFs: x_std1, y_std1, x_std3, y_std3 and x_std10, y_std10, respectively.
Plot all three CDFs as dots (do not forget the marker and linestyle keyword arguments!).
Hit submit to make a legend, showing which standard deviations you used, and to show your plot. There is no need to label the axes because we have not defined what is being described by the Normal distribution; we are just looking at shapes of CDFs.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Generate CDFs




# Plot CDFs



# Make a legend and show the plot
_ = plt.legend(('std = 1', 'std = 3', 'std = 10'), loc='lower right')
plt.show()

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