Recommended prerequisite for participation in the module

The module builds on knowledge acquired in the modules “Programming for Data Wrangling and Visualisation”, “Introduction to statistics”, and “Introduction to AI techniques”.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Different types of regression models, e.g., non-linear regression, logistic regression, penalised regression.
• General principles for parameter estimation and their variance, e.g., maximum likelihood estimation.
• Missing data mechanisms and its limitations and remedies (e.g., multiple imputation).
• Different types of correlated data (e.g., time series) and techniques to analyse such data (e.g. pairwise differences and within-subject summary statistics).

Skills

• Formulate a relevant statistical model for a certain problem.
• Estimate parameters for different kinds of statistical models, including quantifying their variance.

Competences

• Assess the applicability of a statistical model in a given situation.
• Strategies for handling data with missing values in statistical analyses.

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Extent and expected workload

Expected module workload is 150 hours.

Exam

Exams

Name of exam	Advanced Statistics
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures