The Normal PDF

In this exercise, you will explore the Normal PDF and also learn a way to plot a PDF of a known distribution using hacker statistics. Specifically, you will plot a Normal PDF for various values of the variance.

This exercise is part of the course

Statistical Thinking in Python (Part 1)

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Exercise instructions

Draw 100,000 samples from a Normal distribution that has a mean of 20 and a standard deviation of 1. Do the same for Normal distributions with standard deviations of 3 and 10, each still with a mean of 20. Assign the results to samples_std1, samples_std3 and samples_std10, respectively.
Plot a histogram of each of the samples; for each, use 100 bins, also using the keyword arguments density=True and histtype='step'. The latter keyword argument makes the plot look much like the smooth theoretical PDF. You will need to make 3 plt.hist() calls.
Hit submit to make a legend, showing which standard deviations you used, and 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 PDFs.

Hands-on interactive exercise

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

# Draw 100000 samples from Normal distribution with stds of interest: samples_std1, samples_std3, samples_std10




# Make histograms




# Make a legend, set limits and show plot
_ = plt.legend(('std = 1', 'std = 3', 'std = 10'))
plt.ylim(-0.01, 0.42)
plt.show()

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