Probability is useful to quantify uncertainty and describe random events and outcomes. We'll cover ways of calculating probabilities using formulas along with Python functions, how to visualize these random outcomes on a distribution, and how to represent a population using sample data.
Probability allow us to measure uncertainty. This is essential in many different industries, such as meteorology, medicine, sports, insurance, and countless more.
We need to know how:
Define probability using the law of large numbers
Quantify uncertain events using:
The addition rule
The product rule
Bayes' Theorem
Tree Diagrams
Describe random events using probability distributions
Understand use-cases for the following probability distributions:
Binomial Distribution
Poisson Distribution
Describe a population using a sampling distribution and the central limit theorem
Probability is useful to quantify uncertainty and describe random events and outcomes. We'll cover ways of calculating probabilities using formulas along with Python functions, how to visualize these random outcomes on a distribution, and how to represent a population using sample data.
Probability allow us to measure uncertainty. This is essential in many different industries, such as meteorology, medicine, sports, insurance, and countless more.
We need to know how: