- Prepare (due Mon 9/25)
- Content below
- Sakai quizzes
- Video of the piece that got lost from Wednesday’s class
- Peer Instructions – See on the class forum
- Homework (due Sun 10/1) [Link]
- Worked Examples [Link]
Content (Slides in the Box folder)
5.A – Foundations of Probability (52 min.)
- Outcomes, Events, Probabilities (15 min.)
- Joint and Conditional Probability (11 min.)
- Marginalization and Bayes’ Theorem (15 min.)
- Random Variables and Expectations (11 min.)
5.B – Distributions of Random Variables (46 min.)
- Distributions, Means, Variance (19 min.)
- Monte Carlo Simulation (15 min.)
- Central Limit Theorem (12 min.)
- Slide 26 in the video has a typo that is fixed in the pdf version of the slides on Box. In the video, it says the probability is <= 0.95, but it should say < 0.05.
Helpful YouTube videos to understand nuance with examples
- But what is the Central Limit Theorem?
- This is How Easy It Is to Lie With Statistics
- The medical test paradox, and redesigning Bayes’ rule
- How Long Can We Live?
- Understanding Cancer Survival Rates
Online Textbook and Documentation
You can access an excellent free online textbook on OpenIntro Statistics here, co-authored by Duke faculty. You can pay a suggested but adjustable price for a tablet-friendly pdf, but you can also just get the regular pdf for free. For this module, the following optional readings may be particularly helpful supplements:
- Chapter 3: Probability. This provides more information on many of the topics from the above videos in Foundations of Probability.
- Chapter 4: Distributions of random variables. This provides much more information about particular classic distributions than is provided in 2B.B.1.
- Chapter 5.1: Point estimates and sampling variability. This provides more information on some of the topics from 2B.B.2-3.
In addition, you can find documentation for the two pseudorandom number-generating / sampling libraries in python that we mentioned here: