Learning Goals and Examination

Catalog course description

A course in the basic concepts of applied mathematical statistics: parametric models, estimation, confidence intervals, hypothesis testing. Time permitting we will cover linear models (which include ANOVA, linear regression and polynomial regression).

Prerequisites cover i.a. elementary probability theory (sample space, events, probability), density & distribution functions, conditioning, independence, expectation, samples, parameter estimation, confidence intervals, hypothesis testing, central limit theorem.

Learning goals and examination

After you finish this course, you will be able to:

  • Explain, derive and prove core results of applied statistics, including but not limited to:
  • Point estimators, and their properties – how are the standard computations for normal and binomial distributions derived, and why do they work; computations such as sample means, proportions.
  • Hypothesis testing – how and why do the standard hypothesis tests work?
  • Recognize when the tests and methods you have learned fail to apply.