NMAK11022U  Regression (Reg)

Volume 2019/2020
Education

MSc Programme in Statistics
MSc Programme in Mathematics-Economics

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.
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. 

 

 

The book: Regression with R, by Niels Richard Hansen

Statistik 2 (Stat2) or similar

Academic qualifications equivalent to a BSc degree is recommended.
4 hours of lectures for 7 weeks.
4 hours of exercises for 7 weeks, of which 2 hours are for practical work.
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.

Credit
7,5 ECTS
Type of assessment
Written assignment, 27 hours
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Exam registration requirements

To participate in the final written exam a compulsory practical group project must be approved during the course. If it is not approved the first time it can be handed in a second time.

Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner.
Re-exam

The same as the ordinary exam. If ten or fewer students have signed up for re-exam, the type of assessment will be changed to 25 min. oral exam with 50 min. preparation time and several internal examiners. All aids allowed during preparation time, but only computer allowed during the examination.

If the compulsory practical group project was not approved during the course it must be handed in and approved no later than three weeks before the beginning of the reexamination week.

Criteria for exam assesment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

  • Category
  • Hours
  • Exam
  • 27
  • Lectures
  • 28
  • Preparation
  • 98
  • Project work
  • 39
  • Theory exercises
  • 14
  • Total
  • 206