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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  • 25.00 Points
  • 365 Days

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