ST520: Applied Statistics

• It is expected that the students have a mathematical knowledge corresponding to the content in one of the following courses: FF506 Mathematics, statistics and physics for biology and pharmacy; MM554: Mathematics for biology; MM555: Mathematics for Biochemistry and Molecular Biology, Biomedicine and Chemistry; MM556: Mathematics and statistics for pharmacy.
• First year of respective study programmes.

• Understand concepts in probability and distribution theory.
• Utilize graphics and summary methods for descriptive data analysis.
• Describe data using key statistics such as mean, variance, and correlation.
• Construct confidence intervals for key statistics.
• Test simple statistical hypotheses.
• Analyze data using simple regression models.
• Design data collection.
• Understand central elements in published results from statistical analyze of biological data.
• Critically evaluate the appropriateness of employed methods and inferences based on these.
• Present statistical results in non-technical terms.
• Use the statistical software R for analysing actual data, which is important in regard to being able to work academically-scientifically with – in a broad sense – biological problems.

• Give the competence to working critically with own projects and data.
• Give skills to critival evaluate scientific publications.
• Give knowledge and understanding of choice and use of appropriate statistical methods.

The learning objective of the course is that the student demonstrates the ability to:

• Utilizing graphics and summary methods for descriptive data analysis.
• Describing data using key statistics such as mean, variance, and correlation.
• Constructing confidence intervals for key statistics.
• Testing simple statistical hypotheses.
• Analyzing data using simple regression models.
• Designing data collection.
• Understanding central elements in published results from statistical analyses of biological data.
• Critically evaluating the appropriateness of employed methods and inferences based on these.
• Presenting statistical results in non-technical terms.
• Use R for simple statistical analyses.

These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:

• Intro phase (lectures) - 26 hours
• Training phase: 22 hours

In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours aor the study phase hours.

Educational activities 

• Work on specific problems not covered in the training phase hours.
• Discussion of the terms and concepts and problems in regard to data collection and data quality.