ST524: Statistics and Probability Theory

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N360014102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Bachelor

STADS ID (UVA): N360014101
ECTS value: 5

Date of Approval: 24-02-2022


Duration: 1 semester

Version: Archive

Comment

The course runs together with the first half of ST521 Mathematical Statistics. 

Entry requirements

Students who have passed ST521 will not be able to follow the course.

Academic preconditions

Students taking the course are expected to have knowledge of the material from MM533 Mathematical and numerical analysis.

Course introduction

The aim of the course is to enable the student to understand the theory and methods of mathematical statistics, which is important in regard to master the use of these for practical data analysis.
The course builds on the knowledge acquired in the course MM533

Mathematical and numerical analysis, and gives a general introduction into the area of mathematical statistics and as such forms the basis for subsequent statistics courses, like e.g. computational statistics, multivariate analysis, linear models and probability theory, as well as for projects involving statistics.

In relation to the competence profile of the degree it is the explicit focus of the course to:
  • 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

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

January

Tests

Written exam, multiple choice.

EKA

N360014102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Duration

MCQ test 3 hours

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

5

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

60 hours per semester

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

Name E-mail Department
Jing Qin qin@imada.sdu.dk Data Science

Additional teachers

Name E-mail Department City
Wojciech Szymanski szymanski@imada.sdu.dk Analyse
Yuri Goegebeur Yuri.Goegebeur@imada.sdu.dk Data Science

Timetable

Administrative Unit

Institut for Matematik og Datalogi (matematik)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.