NBIA08011U Statistics for Molecular Biomedicine
Volume 2015/2016
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 statistcs 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.
Literature
See Absalon.
Teaching and learning methods
Lectures and interactive
exercises in R.
Remarks
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.
Recommended Reading: Introductory Statistics with R by Peter Dalgaard.
Workload
- Category
- Hours
- Exam
- 60
- Lectures
- 35
- Practical exercises
- 20
- Preparation
- 91
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous Assessment based on three assignments.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners/co-examiners.
- Re-exam
Re-exam will consist of an oral examination (20 min.) with preparation time (20 min.). All aids allowed.
Criteria for exam assesment
In order to achieve the grade 12 the student must be able to demonstrate an excellent fulfillment of the learning outcome described above.
Course information
- Language
- English
- Course code
- NBIA08011U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- A
- Course capacity
- 60 students
- Continuing and further education
- Study board
- Study Board of Biomolecular Sciences and Technology
Contracting department
- Department of Biology
Course responsibles
- Anders Albrechtsen (12-6c6c776d7d706e737f7e70794b6d747a397680396f76)
Saved on the
16-06-2015