ST524: Statistics and Probability Theory
Comment
Entry requirements
Academic preconditions
Students taking the course are expected to have knowledge of the material from MM533 Mathematical and numerical analysis.
Course introduction
- Give the competence to master the theories and methods of mathematical statistics, as well as their application to statistical inference
- Give skills to perform statistical analysis of data and critically argue for the choice between relevant models for analysis and solution
- Give theoretical knowledge and practical understanding of the application of methods and models in mathematical statistics
Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:
- master the theory and methods of mathematical statistics
- master the application of these in data analysis
Content
The following main topics are contained in the course:
- Probability and random variables
- Independence, conditional probability, and Bayes’ Theorem
- Discrete and continuous distributions
- Expectation, variance and covariance
- Special distributions
- The normal distribution and the Central Limit Theorem
- Moment generating functions
- Modes of convergence and the Law of Large Numbers
Literature
Examination regulations
Exam element a)
Timing
Tests
Written exam, multiple choice.
EKA
Assessment
Grading
Identification
Language
Duration
Examination aids
All common aids are allowed e.g. books, notes, computer programmes which do not use internet etc.
Internet is not allowed during the exam. However, you may visit system DE-Digital Exam when answering the multiple-choice questions. If you wish to use course materials from itslearning, you must download the materials to your computer the day before the exam. During the exam itslearning is not allowed.
ECTS value
Additional information
Re-examination:
The re-exam will be changed to an oral exam, if 10 or fewer students are enrolled.
Duration: 30 minutes (including side questions)
- Topics are given before the exam date
- Students draw topic on the exam date
- Students have 30 minutes preparation time before oral exam
Indicative number of lessons
Teaching Method
In order to allow the students to achieve the learning objectives is the teaching organised with 60 hours of lectures and exercises. The teaching activities are reflected in an estimated allocation of the workload of an average student as follows:
- Intro phase (lectures) - 30 hours
- Training phase: 30 hours
Activities during the study phase:
- Work on problems not covered in the training phase.
- Discussion of the concepts and terms of the topic.
Teacher responsible
Additional teachers
Name | Department | City | |
---|---|---|---|
Wojciech Szymanski | szymanski@imada.sdu.dk | Analyse | |
Yuri Goegebeur | Yuri.Goegebeur@imada.sdu.dk | Data Science |