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 Statistics
MSc Programme in Mathematics-Economy

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. 

 

 

5 hours of lectures for 7 weeks.
2 hours of exercises for 7 weeks.

Statistik 2 (Stat2) or similar

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
  • 35
  • Theory exercises
  • 14
  • Project work
  • 39
  • Preparation
  • 91
  • Exam
  • 27
  • English
  • 206