Content, progress and pedagogy of the module

Students who complete the module:

Learning objectives

Knowledge

• Have knowledge of linear models and their role as a general framework for classical statistical modelling techniques, including multiple linear regression (MLR), analysis of variance (ANOVA), and analysis of covariance (ANCOVA).
• Understand the motivation for and key components of selected extensions of linear models, including linear mixed models and multivariate multiple linear regression.
• Have a basic understanding of generalized linear models as well as other statistical modelling frameworks, such as probabilistic graphical models (e.g. Bayesian networks).
• Understand the assumptions, strengths, and limitations of the statistical models covered in the course – including their use for both estimation and prediction.
• Have a basic understanding of principal component analysis (PCA), including its use in both data exploration and as a preprocessing step in modelling.
• Understand the basic principles of Monte Carlo simulation and statistical inference.

Skills

• Can work with matrices – performing fundamental operations and expressing statistical models in matrix form.
• Can formulate and fit standard linear and generalized linear models using appropriate software and interpret the results in a statistically sound manner.
• Can formulate and fit linear mixed models using appropriate software and interpret the results in a statistically sound manner.
• Can formulate and fit multivariate multiple linear regression models using appropriate software and interpret the results in a statistically sound manner. 
• Can critically assess the appropriateness of different modelling approaches for a given problem.
• Can perform and interpret PCA to identify underlying structure in high-dimensional data.
• Can use Monte Carlo simulation to explore uncertainty.
• Can apply core principles of statistical inference, including estimation and hypothesis testing, in the context of the models covered.

Competences

• Can apply statistical modelling techniques to analyse complex data structures in a transparent and reproducible way.
• Are able to communicate modelling assumptions, results, and limitations to both technical and non-technical audiences.
• Can reflect on the role of statistical models in decision making and risk assessment.
• Are able to independently acquire further knowledge within applied statistical modelling

Type of instruction

Lectures, practical exercise sessions, self-study and mandatory assignments.

Extent and expected workload

The module is 5 ECTS, corresponding to approximately 150 hours of study. This workload includes lectures, practical sessions, problem solving, project work, and self-study.

Exam

Prerequisite for enrollment for the exam

• A prerequisite for participating in the exam is that students’ responses to the mandatory assignments have been approved by the instructor.

Exams