Introduction to Probability and Statistics
Jeremy Orloff and Jennifer French Kamrin teach this elementary MIT introduction to probability and statistics with applications, covering basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression. The course leans on active learning, using R and RStudio throughout, with dedicated R tutorials on basics, random numbers, functions, for loops, and run-length encoding alongside its reading materials, problem sets, and exams. Interactive Mathlets applets reinforce concepts, and the same materials are mirrored on MIT's Open Learning Library where you can complete graded reading questions and problem checkers without formally enrolling. A recurring example asks which of three oddly numbered six-sided dice you would choose in a two-player highest-roll game, a hook into the course's emphasis on building real intuition about randomness. Finishing it prepares you to apply Bayesian and frequentist statistical methods to real data.