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Samples from a rolled die

Let's work through generating a simulation using the numpy package. You'll work with the same scenario from the slides, simulating rolls from a standard die numbered 1 through 6, using the randint() function. Take a look at the documentation for this function if you haven't encountered it before.

Starting with a small sample and working your way up to a larger sample, examine the outcome means and come to a conclusion about the underlying theorem.

Diese Übung ist Teil des Kurses

Practicing Statistics Interview Questions in Python

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Anleitung zur Übung

  • Generate a sample of 10 die rolls using the randint() function; assign it to our small variable.
  • Assign the mean of the sample to small_mean and print the results; notice how close it is to the true mean.
  • Similarly, create a larger sample of 1000 die rolls and assign the list to our large variable.
  • Assign the mean of the larger sample to large_mean and print the mean; which theorem is at work here?

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

from numpy.random import randint

# Create a sample of 10 die rolls
small = randint(____, ____, ____)

# Calculate and print the mean of the sample
small_mean = ____
print(____)

# Create a sample of 1000 die rolls
large = randint(____, ____, ____)

# Calculate and print the mean of the large sample
large_mean = ____
print(____)
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