Course content

Principles of statistical modelling and inference. Likelihood theory. General theory for generalised linear models, with applications to regression models for normally distributed data, logistic regression for binary and multinomial data, Poisson regression models and log-linear models for contingency tables. Extensions of GLM-theory to, for example, models for over-dispersion and quasi-likelihood estimation.

Learning outcome

1. Knowledge. The student can assess whether a generalised linear model can be used in a given situation and can further carry out and evaluate such a statistical analysis. The student has substantial knowledge of generalised linear models and associated inference and evaluation methods. This includes regression models for Gaussian distributed data, logistic regression for binary and multinomial data, Poisson regression and log-linear models for contingency tables.

2. Skills. The student can assess whether a generalised linear model can be used in a given situation, and can further carry out and evaluate such a statistical analysis.

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 (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

TMA4267 Linear Statistical Models or TMA4255 Applied Statistics.
Good knowledge in R, a software environment for statistical computing and graphics.

Course materials

Will be announced at the beginning of the semester.