Course content

Random vectors. Multivariate normal distribution. Multiple linear regression. Analysis of variance. Multiple hypothesis testing. Design of experiments.

Learning outcome

1. Knowledge. The student has strong theoretical knowledge about the most popular statistical models and methods that are used in science and technology, with emphasis on regression-type statistical models. The statistical properties of the multivariate normal distribution are well known to the student, and the student is familiar with the role of the multivariate normal distribution within linear statistical models. 2. Skills. The student knows how to design an experiment and how to collect informative data of high quality to study a phenomenon of interest. Subsequently, the student is able to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software. The student knows how to present the results from the statistical analyses, and how to draw conclusions about the phenomenon under study.

Learning methods and activities

Lectures, exercises and works (projects). Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination (70%) and works (electronic exercises and/or projects) (30%). The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination. The lectures may be given in English. If the course is taught in English, the exam may be given only in English. Students are free to choose Norwegian or English for written assessments.

Compulsory assignments

• Work

Recommended previous knowledge

The course is based on TMA4240/4245 Statistics, or equivalent.

Course materials

Will be announced at the start of the course.

Credit reductions