In this unit, we will cover fundamental rules of probability including how to describe random events. We will cover topics such as set theory, conditional probability, joint probability, Bayes rule, probability distributions, and sampling distributions. These concepts are important in order to understand the likelihood of events, fit machine learning models, and perform hypothesis tests.
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You will be analyzing the number of defective products made at a factory on a given day. You will be applying various concepts from the Poisson distribution, including random variables, the probability mass function, the cumulative distribution function, and expected values.
Sampling Distributions Dance Party!
Let's investigate some sampling distributions of Spotify data!
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