Learning outcome

After successfully completing the course, the candidate will have achieved the following learning outcomes defined in terms of knowledge, skills and general competence.

The candidate:

- Has basic knowledge of probability and statistics which other courses in the study programme can build on

- Understands the importance of using statistical methods

- Is able to understand and interpret basic statistical concepts

- Can carry out simple analyses

Course Description

The course is divided into two components: Probability and statistical analysis. The course starts with basic probability calculation, and continues with stochastic variables, both discrete (binomial, hypergeometric and Poisson distribution) and continuous probability distribution (normal distribution). Statistical analysis will include estimation, confidence interval, hypothesis testing and regression. In addition, a brief introduction to descriptive statistics is given.

Teaching and Learning Methods

3-4 hours of lectures per week during the spring semester.

Assessment Methods

• Written examination, 4 hours, graded mark
• Work requirements, mandatory assignment that must be approved in order for the student to take the written examination

Minor adjustments may occur during the academic year, subject to the decision of the Dean