Course content

Hypotheses testing. Simple and multiple linear regression. Model choice. Experimental
design, including two-level factorial design and fractions of these. Error propagation formula.
Analysis of variance. Process control. Contingency tables. Non-parametric methods. Use of
statistical computer package.

Learning outcome

1. Knowledge. The student has a good basic understanding of the most popular statistical models and methods that are used in science and technology. This includes testing of hypotheses, linear regression, experimental design, analysis of variance, error propagation,
process control, analysis of contingency tables and non-parametric methods.

2. Skills. The student knows how to design an experiment to study a phenomenon of interest. Further, he or she knows how to collect informative data of high quality, and subsequently to analyse the collected data using statistical software. The student knows how to present the results from the statistical analyses to colleagues within his or her field of study, and how to use the results of the statistical analysis to draw conclusions about the phenomenon under
study.

Learning methods and activities

Lectures, exercises using the computing software MINITAB or R, 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 may be given in English. If the course is taught in English, the exam will be given only in English. Students are free to choose Norwegian or English for written assessments.

Compulsory assignments

• Exercises

Recommended previous knowledge

ST0103 Statistics with Applications/TMA4240 Statistics/4245 Statistics, or equivalent.

Course materials

Will be announced at the start of the semester.

Credit reductions