Objectives and Content
To give an introduction to probability theory and statistical methods, with emphasis on the former
The main emphasis in this course is on probability models. Discrete and continuous distributions, among others the binomial, the hypergeometric, the exponential, the Poisson and the normal distributions are treated. Joint probability distributions and correlation are also covered. Examples are given from many areas. The last part of the course deals with principles for estimating unknown quantities using maximum likelihood, with confidence intervals, and with hypothesis testing.
On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- Fundamental concepts in probability, such as expectation, variance and correlation.
- Discrete and continuously distributed random variables
- Law of large numbers and the central limit theorem
- Joint and conditional distributions
- Parameter estimation and confidence intervals
- Hypothesis tests and p-values
- Has a practical understanding of the probability concept as it used broadly in society
- Perform and interpret statistical analyses
Compulsory Assignments and Attendance
Forms of Assessment
Written examination, 5 hours.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam. The exam location will be published 14 days prior to the exam. Candidates must check their room allocation on Studentweb 3 days prior to the exam.
Type of assessment: Written examination
- 12.02.2020, 09:00
- 5 hours
- Withdrawal deadline