Entry requirements

General entry requirements

Learning outcomes

Aims

The course gives basic skills in statistical data analysis that can be used at analysis of various types of biomedical data.

SKILLS AND ABILITY

The student should be able to apply on completion of the course

- elementary probability theory

- mathematical distribution models

- inference with distribution models

- interval estimations

- linear regression analysis

- basic multivariate analysis

KNOWLEDGE AND UNDERSTANDING

The student should on completion of the course

- be able to account for the role in statistical inference theory and interpretation of confidence intervals of distribution models and regression models

- be able to account for the difference between frequently and Bayesian statistics.

Content

Applied statistical inference theory including parameter estimation, regression analysis and parametric and non-parametric hypothesis testing. Regression. Performance measurements such as sensitivity, specificity, positive predictive value. Bayesian inference. Orientating introduction to multivariate data analysis in the form of e g hierarchical cluster analysis and multivariate regression.

Instruction

Teaching includes lectures, computer and calculation exercises, problem-oriented group assignments and seminars. Attendance is compulsory at computer and calculation exercises and group assignments and seminars.

Assessment

For a Pass grade in the course, passed results of all compulsory parts and examination are required.

Possibility to supplement failed computer exercises can be given at the earliest at the next course date and only in case of a vacancy.