Syllabus for Multivariate Statistical Analysis

Multivariat statistisk teori och metodik

  • 7.5 credits
  • Course code: 2ST125
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2020-03-13
  • Established by:
  • Revised: 2021-10-15
  • Revised by: The Department Board
  • Applies from: week 27, 2022
  • Entry requirements: 120 credits including 90 credits in statistics and 7.5 credits linear algebra
  • Responsible department: Department of Statistics

Learning outcomes

After completing the course the student is expected to
- know and be able to use multivariate statistical methods
- know to the underlying theory of multivariate statistical methods

Content

Multivariate data; Basic measures and statistics
Inference for one- and two-means; Profile analysis; Multiple comparisons
Multivariate general linear model: MANOVA and Multivariate regression
Principal components analysis
Canonical correlation analysis
Discriminant analysis

Instruction

Teaching can be given in the form of lectures, calculation exercises and computer exercises.

Assessment

The examination takes place partly through a written examination at the end of the course and/or through presentation orally and in written form of compulsory assignments.

Other directives

The course is included in the Master's programme in statistics.

Reading list

The reading list is missing. For further information, please contact the responsible department.