NSCPHD1261  Statistical methods for the Biosciences II - SmB II (generic course) - LPhD015

Volume 2015/2016


PLEASE NOTE         

The PhD course database is under construction. If you want to sign up for this course, please click on the link in order to be re-directed. Link: https://phdcourses.ku.dk/nat.aspx

The principal course content is the project work where the students under supervision analyze their own datasets; suitable datasets should be of moderate size and moderately complicated from a statistical point of view. In addition up to two course days are used to introduce statistical methods which are needed for the projects but not taught on SmB I. Examples of such methods are: analysis of longitudinal data and of repeated measurements, discriminant analysis as a supplement to logistic regression, multivariate methods like PCA, survival analysis. In order to identify the topics to be taught on SmB II the participants must hand in a project synopsis no later than 2 weeks prior to the first course day. Details on this are given on the course home page.

Learning Outcome

After course completion the students are expected to be able to:

- Describe the elements of frequentist statistics including estimation, confidence intervals, hypotesis tests, model validation.
- Describe the statistical models most commonly used in their own field of research.

- Use a statistical software package like R or SAS to perform statistical analysis of their own datasets.

- Formulate scientific questions from their PhD project in terms of statistical hypothesis.
- Interpret the results of a statistical analysis in relation to their PhD project.


'A First Guide to Statistical Computations in R', by Torben Martinussen, Ib Michael Skovgaard, and Helle Sørensen, Biofolia 2012.
R and RStudio is free and open source, and may be downloaded from the internet.

The number of participants is limited at 20, and priority will be given to students who follow SmB I in the same year.
The course days will be a mixture of lectures and exercises including use of computers, and participants must bring their own laptops with R and RStudio installed. During the project period the participants are entitled to two individual supervision meetings with the course lecturer. The projects must result in an article style report and presented before the entire class at the concluding examination seminar. The teaching will be based on the software package R, but the projects may be done using statistical software of the participants own choice. The lecturer has concrete experience with the software packages R, SAS and JMP.
Type of assessment
Written assignment
Oral examination
An evaluation in form of passed/failed is based on the written report and the oral presentation.
Marking scale
passed/not passed
Censorship form
No external censorship
Exam period

One internal examiner


If the student didn't pass based on the written report and the oral defense, then the student has the possibility of resubmitting the report based on the feedback from the examination. The resubmitted report then has to be sufficiently elaborate by itself in order to pass the reevaluation.

  • Category
  • Hours
  • Course Preparation
  • 7
  • Lectures
  • 4
  • Theory exercises
  • 4
  • Project work
  • 60
  • Exam
  • 5
  • Total
  • 80