Regression (Reg)
Course content
- 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
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.
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
Mathematical Statistics or similar
Academic qualifications equivalent to a BSc degree is
recommended.
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.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 27 hours---
- Aid
- 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)
- Category
- Hours
- Lectures
- 28
- Preparation
- 98
- Theory exercises
- 14
- Project work
- 39
- Exam
- 27
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK11022U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 1
- Schedulegroup
-
C
- Capacity
- No limit
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Susanne Ditlevsen (7-787a786673736a457266796d33707a336970)
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Courseinformation of students