NMAK14003U Applied Statistics (AppStat)

Volume 2024/2025

MSc Programme in Agriculture
MSc Programme in Environmental Science
MSc Programme in Environment and Development
MSc Programme in Global Environment and Development


Each student carries out a statistical project (in a group) related to an experiment or a numerical investigation preferably delivered by one of the students in the group. A report is written in journal style and presented orally. Besides, a number of statistical themes are taught at lectures and exercise classes: Data types, comparison of two samples by parametric and non-parametric methods, analysis of tables of counts, regression analysis of categorical data, linear and multilinear regression, analysis of variance, basic design of experiments, usage of random effects, and analysis of longitudinal data and of repeated measurements. The student is also introduced to practical techniques for analyzing data in the open source software package R using the RStudio interface.

Learning Outcome

The course aims at giving the student experience of carrying out statistical analyses.

After completing the course the student should be able to:


- recognize certain data types, identify and specify appropriate statistical models, and argue for the appropriateness.

- explain the prerequisities, prospects and limitations of the methods.


- formulate relevant problems and choose an appropriate statistical model addressing these problems.

- carry out the actual analysis (computations). This includes model fitting, model validation and hypothesis testing.

- extract relevant estimates, draw conclusions and communicate the results from the analysis.

- use the statistical programming language R to carry out the analyses.


- independently formulate scientifically relevant questions - motivated by data of similar types as those presented in the course - and answer them by the use of statistical methods.

The student must have followed an introductory course in statistics and therefore know the basic statistical concepts (variation, estimation, confidence intervals, hypothesis tests) and have experience with simple statistical models (at least oneway ANOVA, linear regression).

Academic qualifications equivalent to a BSc degree is recommended.
During the first part of the course lectures and practical (computer) exercises will run parallel with the initial part of the project work, while the second part will concentrate on the projects. At the oral exam the students will make individual conference-style presentations of their projects.
  • Category
  • Hours
  • Lectures
  • 24
  • Preparation
  • 70
  • Theory exercises
  • 24
  • Project work
  • 84
  • Guidance
  • 3
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Written assignment
Oral examination, 30 minutes
Type of assessment details
Description of Examination: Each group writes a report in a journal paper format about their project. At the oral defense the students make individual conference style presentations of their projects with emphasis on the statistical issues. The oral examination is without preparation and divided into 20 minutes presentation by the student and 10 minutes questioning from the examiner. The grade is awarded on the basis of an overall assessment of the report and the oral exam.
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Internal examiners

As the ordinary exam. If the student has not handed in a passable report during the course, the student must hand in a report no later than three weeks before the beginning of the re-exam week.

Criteria for exam assesment

In order to pass the course the student should hand in a passable report and demonstrate the knowledge, skills and competences described under Learning Outcome at the oral examination.