- 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.
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
- 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.
4 hours of lectures for 7 weeks.
4 hours of exercises for 7 weeks, of which 2 hours are for practical work.
The book: Regression with R, by Niels Richard Hansen
StatMet and MStat (alternatively MatStat from previous years) or
Academic qualifications equivalent to a BSc degree is recommended.
This course is equivalent to NMAB22011U Regression for Actuaries (RegAct)
The mandatory group project will have mandatory feedback by other students in the course. Practice quizzes 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.
- 7,5 ECTS
- Type of assessment
- Type of assessment details
- The continuous assessment is composed of three elements that
are to be completed during the course. The three elements consist
of an overall evaluation of 2 out of 3 individual quizzes and a
group assignment. The quizzes will be of one hour each, which will
be taken as part of the teaching and under surveillance in weeks 4,
6 and 8. The group assignment should be handed in twice. The first
time it will be handed in for peer-review by other students. The
assignment will be handed in a second time after taking the
feedback into account. The final evaluation of the assignment is
exclusively based on the second hand-in. In the group assignment
the contributions from each student have to be clearly stated. Each
quiz as well as the group assignment will be evaluated and assigned
points between 0 and 100. Each element is passed if it obtains at
least 50 out of 100 points.
Each of the three elements must be passed separately to pass the course.
For the final grade the two best results from the quizzes will each count 25% in the final grade and the group assignment will count 50% in the final grade.
If the group assignment is passed, it is valid for the reexam the same year and the ordinary exam the year after.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner.
Criteria for exam assessment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Single subject courses (day)
- Theory exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 1
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Susanne Ditlevsen (7-75777563707067426f63766a306d7730666d)
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Courseinformation of students