NSCPHD1060 Statistical methods for the Biosciences I - SmB I (generic course) - LPhD014

Volume 2015/2016
Content

 

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The course covers basic techniques in model based frequentist statistics exemplified by real applications from the biosciences. Topics covered are: Descriptive statistics, data types, comparison of two samples by parametric and non-parametric methods, analysis of tables of counts, regression analysis of categorical data, linear and multilinear regression, analysis of variance, basic design of experiments, and usage of random effects. The student is also introduced to practical techniques for analyzing data in the open source software package R using the RStudio interface. Recommended prerequisites for the course is some basic statistics course during the participants bachelor or master studies.

Learning Outcome

The students are introduces to statistical models commonly used in the biosciences for univariate end-points. The statistical methodology is discussed with emphasis on how models are applied, and the students are trained to do the statistical analyses using the software package R.

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

Knowledge:
- Describe the elements of frequentist statistics including estimation, confidence intervals, hypotesis tests, model validation.
- Describe the discussed data types.
- Describe the assumptions behind the discussed statistical models.

Skills:
- Identify the data type in a particular dataset, and formulate an adequate statistical model.
- Use R via the RStudio interface to perform the statistical analysis.

Compentences:
- Formulate scientific questions in terms of statistical hypothesis.
- Conduct statistical analysis using the discussed models.
- Interpret the results of a statistical analysis.
- Critically reflect over the results, conclusions and limitations of a statistical analysis.
- Judge when to seek help from a skilled statistician.

 

 

Literature

 '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.

Lectures and exercises including use of computers. In the first half of the course days focus will be on lectures, and in the second half on individual coursework with exercises. But we will switch between these two modes of teaching during the entire day. Participants must bring their own laptops with R and RStudio installed.
  • Category
  • Hours
  • Lectures
  • 20
  • Preparation
  • 60
  • Theory exercises
  • 20
  • Total
  • 100
Credit
4,5 ECTS
Type of assessment
Continuous assessment
Exam registration requirements

The course is graded as passed/failed. To pass the student must participate in 3 of the 5 course days.

Marking scale
passed/not passed
Censorship form
No external censorship
One internal examiner.