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

Mathematical Statistics or similar

Academic qualifications equivalent to a BSc degree is recommended.

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

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
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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