DS805: Multivariate Statistical Analysis

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N370009102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N370009101
ECTS value: 5

Date of Approval: 02-10-2019


Duration: 1 semester

Version: Archive

Comment

The course is co-read with: ST514 and ST811 Multivariate statistical analysis.

Entry requirements

The course cannot be chosen by students who have passed ST514 or ST811.

The course cannot be taken by students enrolled in the master programme in Computer Science

Academic preconditions

Students taking the course are expected to:

  • Have skills in
    statistics and mathematics corresponding to DS803 Statistics for Data Science 
  • Be able to use the statistical software R.

Course introduction

The aim of the course is to enable the student to work systematically
with data sets with several variables, which is important in regard to
performing statistical analyses in a broad range of research areas, such
as biology and epidemiology.

The course builds on the knowledge
acquired in basic statistics courses such as DS803 Statistics for Data Science,and gives an academic basis for studying more advanced topics in statistics,as well as working on master
projects where multivariate methods are employed.


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


  • Give the competence to evaluate and choose between different methods for the analysis of multivariate data sets.
  • Give skills to perform analyses of multivariate data sets using the statistical software R.
  • Give knowledge and understanding of the fundamental ideas and methods for analyzing measurements on several variables.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

  • Reproduce key theoretical results concerning elementary operations on random variables and random vectors.
  • Work with the concepts and models, both in scalar and matrix/vector representation.
  • Understand and identify problems that can be solved using multivariate techniques.
  • Perform a practical data analysis with the techniques from the course.
  • Perform programming relevant to the content of the course in the statistical package used in the course.
  • Identify and interpret relevant information in the output of the statistical package used in the course.
  • Summarize the results of an analysis in a statistical report.

Content

The following main topics are contained in the course:
  • random vectors
  • the multivariate normal distribution
  • inference about a mean vector
  • comparison of several mean vectors
  • principal component analysis
  • discriminant analysis and classification

Literature

See Blackboard for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

June

Tests

Project and written exam

EKA

N370009102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Additional information

The examination is based on written project reports handed in during the course and a written exam. 

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

48 hours per semester

Teaching Method

In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours aor the study phase hours.

Educational activities

  • 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 Institut for Matematik og Datalogi

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile Education Semester Offer period