MIT Course – Broad Course Objectives
- Learn the language and core concepts of probability theory.
- Understand basic principles of staotistical inference (both Bayesian and frequentist).
- Build a starter statistical toolbox with appreciation for both the utility and limitations of these techniques.
- Use software and simulation to do statistics (R).
- Become an informed consumer of statistical information.
- Prepare for further coursework or on-the-job study.
Specific Learning Objectives
Students completing the course will be able to:
- Use basic counting techniques (multiplication rule, combinations, permutations) to compute probability and odds.
- Use R to run basic simulations of probabilistic scenarios.
- Compute conditional probabilities directly and using Bayes’ theorem, and check for independence of events.
- Set up and work with discrete random variables. In particular, understand the Bernoulli, binomial, geometric and Poisson distributions.
- Work with continuous randam variables. In particular, know the properties of uniform, normal and exponential distributions.
- Know what expectation and variance mean and be able to compute them.
- Understand the law of large numbers and the central limit theorem.
- Compute the covariance and correlation between jointly distributed variables.
- Use available resources (the internet or books) to learn about and use other distributions as they arise.
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