Beginning Probability

Tackle Likelihood to Avoid Risk and Make Choices, while Using all You Know and Avoiding Decision-destroying Fallacies

What you'll learn
Avoid the probability fallacies that often mislead people
Calculate probabilities using cases and historical data
Calculate the probabilities of complex events based on simpler ones
Apply Bayes' Rule to bring outside knowledge into your probability estimates
Use expected value to assess and compare alternatives
Focus your analysis on segments or situations using conditional probability
Reason correctly using false positives and false negatives
Develop intuition about randomness
Requirements
No requirements. No math skills needed beyond basic arithmetic. The spreadsheets are optional.
Description
Use the science of probability to turn vague terms like "likely" into precise values you can use to assess risks and alternatives. This beginning course gives you all you need to apply probability to real-world questions. Use Bayes' Rule to incorporate your contextual knowledge. Combine simple probabilities to assess complex events. Use conditional probability to focus on groups or situations. Draw correct conclusions from conditional probabilities, including false positives and false negatives. Avoid the many probability fallacies that often lead to bad decisions. Try out your knowledge in exercises, then take on some tricky challenges (you'll get the solutions). A formula “cheat sheet” and optional spreadsheets are included.What You'll LearnProbability is both a science and a measure of likelihood. We go beyond the standard examples of cards and dice and discuss what it means for weather prediction or medical tests. Probability is useful for giving you an expected value, what you would expect the outcome to be on average. Yet, as we watch random events play out in a simulation, we see that we need to also expect the unexpected.Probability appears simple and straightforward, which leads people to get misled by various common probability fallacies. One key fallacy is seeing patterns that aren't there. You will see examples in which ordinary randomness leads to silly conclusions. Another is the confusion of the inverse of conditional probabilities. This causes confusion in assessing the results of medical tests and making faulty causal connections.From Your Instructor, Carol JacobyI’ve been using various types of analysis to answer tricky questions for over 30 years. I did this as a mission analyst at Hughes Electronics and other companies to predict outcomes and compare alternatives. The applications were broad and ill-defined: protect Europe from missile attack, limit drug smuggling, design a highway system for self-driving cars and more. I have a PhD in mathematics, and I’ve been teaching technical classes to managers through major universities for 20 years. The students praise my enthusiasm and ability to make complex subjects clear. A common comment is, "I wish you had been my math teacher in high school." Here are samples of classes that were heavy in analysis.· Predictive Analytics: Caltech Center for Technology and Management Education· Lean Six Sigma: Caltech Center for Technology and Management Education· Systems Engineering: UCLA Extension for Raytheon· The Decisive Manager: UCLA Technical Management ProgramOne thing I like about teaching is interacting with the students. I look forward to comments and direct messages and respond promptly. Any feedback is encouraged. If something is confusing or doesn’t work as expected, I want to hear about it right away so I can fix it. I especially want to hear about your own data explorations and other topics you’d like to learn about or problems you’d like to solve.So, are you ready to dig into that data and see what you can learn? Learn probability in just a couple hours. Sign up now. If you want more of a taste first, check out the quick promo video or some of the free lessons. I hope to see you in class.

Overview

Section 1: Introduction

Lecture 1 Introduction: Welcome

Lecture 2 Probability: What and Why/Expected and Unexpected Values

Lecture 3 3. Probability and the Real World/It’s not Just Count and Divide

Section 2: Probability Fallacies and How to Avoid Them

Lecture 4 4. Common Fallacies/Simple but Prevalent Misuses of Probability

Lecture 5 Key Fallacies/Spurious Patterns and Confusion of the Inverse

Section 3: Conditional Probabilities

Lecture 6 6. Conditional Probabilities/When the Situation or Group Being Considered Makes

Lecture 7 False Positives and False Negatives/How Good is the Prediction Really?

Lecture 8 COVID Example and Summary of False Positives and False Negatives

Section 4: Calculating Probabilities

Lecture 9 9. Calculating Simple Probabilities/Equally Likely Outcomes and Historical Data

Lecture 10 10. Combining Probabilities to Calculate More Complex Ones

Lecture 11 Try It/Examples of the Techniques You’ve Learned

Section 5: Bayes' Rule

Lecture 12 Bayes’ Rule/Enriching Your Probabilities with Outside Knowledge

Lecture 13 Using Bayes’ Rule/Solving Confusion of the Inverse and False Positives

Section 6: Expected Value

Lecture 14 Expected Value/How Much Can You Expect on Average

Section 7: The Wrap-up

Lecture 15 Challenges/The Wedding Challenge and the Birthday Challenge

Lecture 16 The Monty Hall Problem

Lecture 17 17. Summary/Key Concepts and Formulas

Lecture 18 18. Bonus Lesson

Beginner interested in more precise likelihood prediction,Beginner who needs a probability foundation for statistics, risk analysis, data science or other more advanced analysis.