In this lesson, you'll be able to build on what you learned in the conditional probability: fundamentals lesson. In the first two steps of this lesson, you'll spend time getting a better understanding of the ins and outs of conditional probability. In addition to spending more time with calculations, you will spend time understanding the subtle differences between terminology in probability.
You will learn the multiplication rule of probability, or multiplication rule, a critical rule when calculating probability and an important concept in terms of conditional probability. Even though, we discussed the multiplcation rule in the Probability Fundamentals course, we will take the time to clarify the distance in this lesson.
In addition to the multication rule of probability, you will learn what it means for two events to be statiscally independent, or independent, of each other as well as other concepts essential for understand conditional probabilitty.
Even though this lesson uses a lot of symbols and mathematical terminology, you won't feel lost with the explanations as they are easily digestible and understandable. And if you still feel lost, feel free to go back and review our probability fundamentals course.
Even though there is no dataset in this lesson, you will still have the opportunity to complete exercises as you go through this lesson. By the end of this lesson, you will be able to feel completely confident with the fundamentals of conditional probability and be well-equipped to better estimate probabilities.
As you work with through learning more of conditional probability, you’ll get to apply what you’ve learned from within your browser so that there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next concept.
1. An Important Difference
3. Order of Conditioning
4. The Multiplication Rule
5. Statistical Independence
6. Statistical Dependence
7. Independence for Three Events
8. Formula for Three Dependent Events
9. Next steps