Beginning Probability
Beginning Probability, available at $54.99, has an average rating of 4.83, with 18 lectures, 6 quizzes, based on 9 reviews, and has 51 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner interested in more precise likelihood prediction or Beginner who needs a probability foundation for statistics, risk analysis, data science or other more advanced analysis. It is particularly useful for Beginner interested in more precise likelihood prediction or Beginner who needs a probability foundation for statistics, risk analysis, data science or other more advanced analysis.
Enroll now: Beginning Probability
Summary
Title: Beginning Probability
Price: $54.99
Average Rating: 4.83
Number of Lectures: 18
Number of Quizzes: 6
Number of Published Lectures: 18
Number of Published Quizzes: 6
Number of Curriculum Items: 24
Number of Published Curriculum Objects: 24
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Beginner interested in more precise likelihood prediction
- Beginner who needs a probability foundation for statistics, risk analysis, data science or other more advanced analysis.
Target Audiences
- Beginner interested in more precise likelihood prediction
- Beginner who needs a probability foundation for statistics, risk analysis, data science or other more advanced analysis.
Use the science of probability to turn vague terms such as “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 what you know about the outside world. Combine simple probabilities to find the likelihood of 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 Learn
Probability 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 Jacoby
I’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 Program
One 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.
Course Curriculum
Chapter 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
Chapter 2: Probability Fallacies and How to Avoid Them
Lecture 1: 4. Common Fallacies/Simple but Prevalent Misuses of Probability
Lecture 2: Key Fallacies/Spurious Patterns and Confusion of the Inverse
Chapter 3: Conditional Probabilities
Lecture 1: 6. Conditional Probabilities/When the Situation or Group Being Considered Makes
Lecture 2: False Positives and False Negatives/How Good is the Prediction Really?
Lecture 3: COVID Example and Summary of False Positives and False Negatives
Chapter 4: Calculating Probabilities
Lecture 1: 9. Calculating Simple Probabilities/Equally Likely Outcomes and Historical Data
Lecture 2: 10. Combining Probabilities to Calculate More Complex Ones
Lecture 3: Try It/Examples of the Techniques You’ve Learned
Chapter 5: Bayes' Rule
Lecture 1: Bayes’ Rule/Enriching Your Probabilities with Outside Knowledge
Lecture 2: Using Bayes’ Rule/Solving Confusion of the Inverse and False Positives
Chapter 6: Expected Value
Lecture 1: Expected Value/How Much Can You Expect on Average
Chapter 7: The Wrap-up
Lecture 1: Challenges/The Wedding Challenge and the Birthday Challenge
Lecture 2: The Monty Hall Problem
Lecture 3: 17. Summary/Key Concepts and Formulas
Lecture 4: 18. Bonus Lesson
Instructors
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Carol Jacoby
Excel for Decision Makers
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- 4 stars: 2 votes
- 5 stars: 7 votes
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