Introduction to Probability and Statistics
A unified introduction to probability, Bayesian inference, and frequentist statistics. Topics include: combinatorics, random variables, (joint) distributions, covariance, central limit theorem; Bayesian updating, odds, posterior prediction; significance tests, confidence intervals, bootstrapping, regression. Students also develop computational skills and statistical thinking by using R to simulate, analyze, and visualize data; and by exploring privacy, fairness, and causality in contemporary media and research. Flipped subject taught in a Technology Enabled Active Learning (TEAL) classroom to facilitate discussion, group problem solving, and coding studios with ample mentorship.
Spring 2022
This course provides an elementary introduction to probability and statistics with applications. Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression. These same course materials, including interactive components (online reading questions and problem checkers) are available on MIT’s {{% resource_link "c2c98a05-5a25-4ff4-adc9-82cc906c44ce" "Open Learning Library" %}}, which is free to use. You have the option to enroll and track your progress, or you can view and use the materials without enrolling.
Readings
- There will be no assigned textbook for the class. will be assigned.
Prerequisites & course information
Prereq: Calculus II (GIR) U (Spring) 4-0-8 units. REST
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2022
- MIT OCW calendar ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.