NMAK11022U Regression (Reg)
MSc Programme in Statistics
MSc Programme in Mathematics-Economy
- Multiple linear regression and least squares methods.
- Generalized linear models.
- Survival regression models.
- Nonlinear effects and basis expansions.
- Parametric, semiparametric and nonparametric likelihood methods.
- Aspects of practical regression analysis in R.
- Linear, generalized linear and survival regression models.
- Exponential dispersion models.
- Likelihood, quasi-likelihood, nonparametric likelihood and partial likelihood methods.
Skills: Ability to
- perform a mathematical analysis of likelihood functions in a regression modeling context.
- compute parameter estimates for a regression model.
- perform model diagnostics, statistical tests, model selection and model assessment for regression models.
- construct confidence intervals for a univariate parameter of interest in theory as well as in practice.
- use R to be able to work with the above points for practical data analysis.
Competences: Ability to
- construct regression models using combinations of linear predictors, basis expansions, link-functions and variance functions.
- interpret a regression model and predictions based on a regression model.
- evaluate if a regression model is adequate.
2 hours of exercises for 7 weeks.
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hours---
- Exam registration requirements
To participate in the final written exam a compulsory practical group project must be approved during the course. If it is not approved the first time it can be handed in a second time.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
25 min. oral exam with 50 min. preparation time and several internal examiners. All aids allowed during preparation time, but only computer allowed during the examination.
If the compulsory practical group project was not approved during the course it must be handed in no later than two weeks before the beginning of the reexamination week. It has to be approved before the reexamination.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
- Theory exercises
- Project work