Course content

The course includes chi-squared tests (test for probabilities in multinomial models, model test with known and unknown parameters, test for homogeneity, test for independence) regression analysis, design of experiments (including analysis of variance and 2^k experiments), generalised linear models, transformations for normal approximations, variance stabilising transformations, non-parametric tests, approximations for mean and variance (including for estimators implicitly defined by an equation), combination of estimators, combination of tests, multiple testing (including Bonferroni correction), simulation tests and bootstrapping.

Learning outcome

The objective of the course is to give the students a solid foundation in a broad variety of important statistical methods that can be used in technology, industry and biomodelling. In addition the students shall be capable of planning collection of data and to use statistical software for analysing data. The course is primarily for students in the two study programs Mathematics and statistics and Biomathematics.

Learning methods and activities

Lectures and compulsory exercises. The final grade is a weighted sum based on both the written final exam (80%) and on a grade in the project (20%).

Retake of examination may be given as an oral examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

The course is based on knowledge in mathematics similar to MA0001/0002 Mathematical methods A/B or MA1101/1102 Basic calculus I/II, and knowledge of statistics similar to ST0101 Probability with applications/0201 Statistics with applications, alternatively ST1101 Probability/ST1201 Statistical methods or ST0202 Statistics for social sciences.

Course materials

Will be announced at the start of the course.

Credit reductions