# Statistics for Molecular Biomedicine

### Course 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
Education

MSc Programme in Molecular Biomedicine
MSc Programme in Bioinformatics

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.

Lectures and interactive exercises in R.

See Absalon.

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.

ECTS
7,5 ECTS
Type of assessment
Continuous assessment
Continuous Assessment based on three assignments.
Marking scale
Censorship form
No external censorship
Several internal examiners/co-examiners.
##### Criteria for exam assessment

In order to achieve the grade 12 the student must be able to demonstrate an excellent fulfillment of the learning outcome described above.

Single subject courses (day)

• Category
• Hours
• Exam
• 60
• Preparation
• 91
• Lectures
• 35
• Practical exercises
• 20
• English
• 206