Recommended prerequisite for participation in the module

Basic knowledge about Python is recommended, including running Python scripts and basic data structures. This can be obtained, e.g., in online material that will be made available to the students.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Probability and uncertainty, including expected values, variance, independence, conditional probabilities, and Bayes theorem.
• Discrete and continuous probability distributions, including the normal (Gaussian) distribution and binomial distribution.
• The central limit theorem.
• Linear regression model, including handling of qualitative variables (e.g., via dummy variables).
• Statistical inference, including hypothesis testing and confidence intervals.
• Data collection and bias (types of and sources).

Skills

• Account for the basic theory and assumptions behind linear regression models.
• Estimate linear regression model parameters, standard errors, and construct confidence intervals using data.
• Use a linear regression model as a prediction model.
• Use relevant statistical software.

Competences

• Assess the applicability of linear regression model in a given situation.
• Use correct professional terminology including discussion of possible biases.
• Acquire additional knowledge in the field.

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	Introduction to Statistics
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures