NMAK14028U  Project in Statistics

Volume 2018/2019

MSc Programme in Statistics
MSc Programme in Mathematics-Economics


This project course on advanced statistical modeling is a compulsory part of the MSc program in statistics. The content of the course presumes a mathematical level corresponding to this MSc program, and consists of:

  • An advanced subject in statistical modeling.
  • Practical work with statistical modeling, implementation and/or data analysis.
  • Independent literature studies.
  • Written presentation of data analysis and writing of reports.


The exact content depends on the supervisors assigned to the course. A list of projects will be offered at the beginning of the course, and the participants choose one from the list.

Examples of advanced subjects are:

  • Classification and Machine Learning.
  • Functional data analysis.
  • Bayesian analysis and Markov Chain Monte Carlo (MCMC).
  • Graphical and hierarchical models.
  • Applied biostatistics.
  • Dynamical systems.
Learning Outcome


  • Aspects of applied statistics

Skills: Ability to

  • indepencently read graduate level statistics literature
  • apply statistical models to data analysis


Competences:  Ability to

  • organize a practical data analysis and a corresponding literature study under supervision
  • document the process in at coherent report
  • be able to prioritize efforts in the data analysis as well as in the documentation so that the reader can assess and if necessary reproduce the analysis.
The participants are expected to have the mathematical and statistical competences of a MSc student in statistics and have passed the courses DisMod and Regression (Reg).
Practical project under supervision.
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written assignment
Final individual report
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship

If the project report is not passed, an new report must be handed in. It can be based on the original report.

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
  • Guidance
  • 14
  • Project work
  • 190
  • Lectures
  • 2
  • Total
  • 206