NMAK11022U Regression (Reg)

Volume 2013/2014
Education
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
Content
  • Multiple linear regression and generalized linear models.
  • Non-linear effects and basis expansions.
  • The statistical analysis of regression models based on variations of likelihood methods.
  • Non-parametric survival methods, parametric and semi-parametric survival analysis.
  • Aspects of practical regression analysis in R.
Learning Outcome
Knowledge:

  • Linear, generalized linear and survival regression models.
  • Likelihood, quasi-likelihood and partial likelihood methods.
  • R.

Skills: Ability to

  • verify if the maximum-likelihood estimator (MLE) exists, compute the MLE and compute quadratic approximations to the log-likelihood.
  • apply standard tools from regression analysis for model diagnostics, statistical tests and model selection.
  • construct confidence intervals for univariate parameters of interest in theory as well as in practice.
  • use standard tools in 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, link-functions and variance functions.
  • interpret a regression model and predictions based on a regression model.
Stat2
5 hours of lectures
  • Category
  • Hours
  • Exam
  • 27
  • Lectures
  • 35
  • Preparation
  • 84
  • Project work
  • 39
  • Theory exercises
  • 21
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Written examination, 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. In addition, a small compulsory theoretical problem must be solved and handed in every week.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner.
Criteria for exam assesment

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