NMAK11022U Regression (Reg)
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
- 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.
Knowledge:
- Linear, generalized linear and survival regression models.
- Exponential dispersion models.
- Likelihood, quasi-likelihood, nonparametric likelihood and partial likelihood methods.
- R.
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.
The book: Regression with R, by Niels Richard Hansen
Academic qualifications equivalent to a BSc degree is recommended.
4 hours of exercises for 7 weeks, of which 2 hours are for practical work.
- Category
- Hours
- Lectures
- 28
- Preparation
- 98
- Theory exercises
- 14
- Project work
- 39
- Exam
- 27
- Total
- 206
The mandatory group project will have mandatory feedback by other students in the course, then a corrected version will be given oral feedback by teachers. Quizz'es will be conducted and discussed at lectures, for the students to understand what they have to work with, evaluate their knowledge and test if they have understood the concepts correctly, as well as to help the teacher with the further organization of the course.
- Credit
- 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.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
- Re-exam
The same as the ordinary exam. If ten or fewer students have signed up for re-exam, the type of assessment will be changed to 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 and approved no later than three weeks before the beginning of the reexamination week.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK11022U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No limit
- Course is also available as continuing and professional education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Susanne Ditlevsen (susanne@math.ku.dk)