Introduction to Probability and Statistics (18.05)

Probability from the ground up

This MIT course is an elementary entry point into probability and statistics, with applications throughout. It begins with counting methods and random variables, then works through distributions, Bayesian reasoning, significance testing and regression.

Who it suits

Students and self-learners who want a solid footing in probability will get the most from the blend of theory and applied examples. Bayesian inference and linear regression sit alongside the classic tests, so several approaches are compared within one course.

Takeaways

• Combinatorics is the counting toolkit beneath much of probability
• Bayesian inference offers an alternative lens on hypothesis testing
• Interactive reading questions and problem checkers support hands-on practice

The materials also sit on MIT's Open Learning Library, where the interactive components are available.