Module 6: Lesson 4

Introduction to Probability Distributions

The final lesson introduces distributions, both empirical and theoretical, which provide concise representations of a data set. Understanding how to use and employ distributions will provide general guidance into modeling and interpreting a data set based on theoretical expectations.

Objectives

By the end of this lesson, you will be able to

  • explain the difference between empirical and theoretical probability distributions,
  • explain the difference between key theoretical distributions like the Uniform, Binomial, Normal, Poisson, and LogNormal,
  • compute statistical descriptions from theoretical distributions,
  • apply QQ plots to test for normality,
  • explain the importance of the Central Limit Theorem, and
  • fit a theoretical distribution to an empirical data set.

Time Estimate

Approximately 2 hours.

Activities

Reading: Explore random variables, discrete, and continuous distributions by using this visual website from seeing-theory.

Video: Watch the introduction to distributions video, which will demonstrate how to compute empirical and theoretical distributions in Python.

Notebook: Read and complete the practice exercises in the Introduction to distributions notebook.

© 2017: Robert J. Brunner at the University of Illinois.

This notebook is released under the Creative Commons license CC BY-NC-SA 4.0. Any reproduction, adaptation, distribution, dissemination or making available of this notebook for commercial use is not allowed unless authorized in writing by the copyright holder.