Short Description

The statistical and probabilistic foundations of inference, developed jointly through mathematical derivations and simulation techniques. Important distributions and large sample results. The frequentist paradigm.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Manipulate the most important probability distributions, and correctly identify appropriate distributions for different modeling situations.
  2. Design, perform and interpret frequentist hypothesis tests in the context of parameter estimation.
  3. Work proficiently with standard statistical notation for parameters, sample quantities, estimators, etc.
  4. Apply large sample results including the Law of Large Numbers and the Central Limit Theorem.
  5. Design and perform appropriate simulation techniques to make predictions and understand the relationship between models and data.

Reference Material

  • TBD

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.