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.
Education

MSc Programme in Mathematics-Economics
MSc Programme in Statistics
 

Learning outcome

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

StatMet and MStat (alternatively MatStat from previous years) or similar

Academic qualifications equivalent to a BSc degree is recommended.

This course is equivalent to NMAB22011U Regression for Actuaries (RegAct)

Oral
Collective
Continuous feedback during the course
Peer feedback (Students give each other feedback)

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.

ECTS
7,5 ECTS
Type of assessment
Continuous 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.
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
  • 97
  • Theory exercises
  • 28
  • Project work
  • 50
  • Exam
  • 3
  • 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
The number of seats may be reduced in the late registration period
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)
Saved on the 07-04-2022

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