On completion of 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).
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).
Lectures, computer sessions. Guest lecture. Case studies where the course content is applied in problems arising in technology, the natural or social sciences.
Written examination at the end of the course (4 credits) combined with assignments given during the course (1 credit).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.