Entry requirements

120 credits including Probability and Statistics

Learning outcomes

In order to pass the course the student should be able to:

• use the most common statistical tests and understand their assumptions and limitations;
• formulate and choose a suitable methodology for testing in a given situation;
• use the most common estimation methods (e.g. method of moments, or the maximum-likelihood method);
• perform estimation in regression models and evaluate a proposed model;
• evaluate results from statistical software (e.g. R).

Content

Statistical hypothesis testing (interpretation with confidence intervals, p-values), estimation methodology (ML and LS estimation); non-parametric methods, correlation analysis, multiple regression (estimation, prediction, diagnostics).

Instruction

Lectures, computer sessions. Guest lecture. Case studies where the course content is applied in problems arising in technology, the natural or social sciences.

Assessment

Written examination at the end of the course (4 credits) combined with assignments given during the course (1 credit)