NBIA08011U Statistics for Molecular Biomedicine

Volume 2013/2014
Education
MSc Programme in Molecular Biomedicine
Content
  • Descriptive statistics
  • Distributions
  • Study design
  • Hypothesis testing/ interval estimation
  • Non-parametric methods
  • Analysis of variance
  • Linear regression
  • Multiple testing
  • The statistical program R
Learning Outcome

Knowledge:
  • Understand statistical models for data of biological and/or medical relevance
  • Understand the symbolic language of statistics and the corresponding formalism for models based on the normal distribution
  • Understand significance testing, p-value calculation and interpretation for experimental data


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 in a coherent and well documented report.
  • Apply R for the practical statistical analysis of biological data such as gene expression data.


Competencies:
General competences in probability and statistics
Literature
See Absalon.
Lectures and interactive exercises in R.
The course will involve interactive R sessions and so students will need to bring a laptop computer to lectures.
Recommended Reading: Introductory Statistics with R by Peter Dalgaard (1st or 2nd ed) (an electronic version is available at the library web site).
  • Category
  • Hours
  • Exam
  • 40
  • Lectures
  • 35
  • Practical exercises
  • 20
  • Preparation
  • 111
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Continuous Assessment: Three take-home assignments during course. Internal censors.
Marking scale
7-point grading scale
Censorship form
No external censorship
Re-exam
Re-exam will consist of an additional 3-day assignment.
Criteria for exam assesment
In order to achieve the grade 12 the student must be able to;
  • 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 in a coherent and well documented report.
  • Apply R for the practical statistical analysis of biological data such as gene expression data.