Course content

Simple linear and multiple linear regression. Analysis of variance. Experimental design. Multivariate normal distribution. Model selection and regularization. Contingency tables.

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 using appropriate software, and semester project(s). Portfolio assessment
is the basis for the grade awarded in the course. This portfolio comprises a written final
examination (80%) and the semester project(s) (20%). 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 will be given in English if they are attended by students from the Master’s Programme
in Mathematical Sciences for International Students.

Compulsory assignments

• Exercises

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