General provisions

The course is given within the master's programme in precision medicine (MPM2M). This motivates the grading system

Entry requirements

General entry requirements and Mathematics 3b or 3c/Mathematics C

Learning outcomes

Knowledge and understanding

A student that has passed the course should

• be able to explain the difference between descriptive, predictive and causal studies and identify which type of study should be used to answer a specific aim.

Competence and skills

A student that has passed the course should

• be able to use basic statistical methods for medical data analysis
• be able to use statistical software for basic analysis of data and statistical modelling
• have the ability to orally and in writing present results from a statistical study

Judgement and approach

A student that has passed the course should

• be aware of limitations in a statistical analysis
• be able to interpret results from a statistical analysis

Content

Descriptive statistics. Types of data sources. Basic inference, such as estimation, test and prediction. Comparing differences in means and proportion in several populations. Correlation. Regression analysis for decription and predictionwith quantitative and binary dependent variables with one or several independent variables.

A number of general science-related questions are discussed during joint seminars, which also include presentation skills and research ethics.

Instruction

Teaching is given in the form of lectures, computer exercises and mandatory seminars.

Assessment

The examination takes place through a written examination at the end of the course and through written and/or oral presentations of mandatory submissions. If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University ́s disability coordinator.