# NBIA08011U Statistics for Molecular Biomedicine

Volume 2019/2020
Education

MSc Programme in Molecular Biomedicine
MSc Programme in Bioinformatics

Content

The course is an introduction to statistics aimed for students of medical and biological sciences. An important part of the course is to learn the practical application of statistics using R, which is an open source statistics program. Topics include:

• Descriptive statistics
• Distributions
• Study design
• Hypothesis testing/ interval estimation
• Non-parametric methods
• Analysis of variance
• Linear regression
• The statistical program R
Learning Outcome

Knowledge:

The student will obtain knowledge of

• Statistics for data of biological and/or medical relevance, in particular
• Descriptive statistics
• Distributions
• Study design
• Hypothesis testing/interval estimation
• Non-parametric methods
• Analysis of variance
• Linear regression
• The symbolic language of statistics and the corresponding formalism for models based on the normal distribution
• Interpretation of statistical results for experimental data
• The R program

Skills:

• Set up statistical models for data of biological and/or medical relevance – taking as a starting point models based on the normal distribution.
• Handle the symbolic language of statistics and the corresponding formalism for models based on the normal distribution and be able to carry out necessary calculations.
• Perform significance testing, p-value calculation and interpretation for simple experimental data, including compute-intensive techniques such as permutation testing.
• Report the results of model set up, data analysis, interpretation and assessment.
• Apply R for the practical statistical analysis of biological data.

Competences:

• Formulate scientific questions in statistical terms.
• Interpret and report the conclusions of a practical statistical analysis.
• Assess and discuss a statistical analysis in a biomedical context.

See Absalon.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures and interactive exercises in R.
The course will involve interactive R sessions, so students will need to bring a laptop computer to lectures.
Recommended Reading: Introductory Statistics with R by Peter Dalgaard.
• Category
• Hours
• Exam
• 60
• Lectures
• 35
• Practical exercises
• 20
• Preparation
• 91
• Total
• 206
Written
Individual
Collective
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Continuous Assessment based on three assignments. The assignments should be done individually and not in groups.
Weight is 30%, 30% and 40% at the third assignment.
Aid
All aids allowed
Marking scale