Syllabus for Multivariate Statistical Analysis

Multivariat statistisk teori och metodik

Syllabus

  • 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: 2022-10-14
  • Revised by: The Department Board
  • Applies from: Autumn 2023
  • Entry requirements:

    120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 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.

Syllabus Revisions

Reading list

Reading list

Applies from: Autumn 2023

Some titles may be available electronically through the University library.

  • Johnson, Richard Arnold; Wichern, Dean W. Applied multivariate statistical analysis

    6. ed.: Upper Saddle River, N.J.: Pearson Prentice Hall, cop. 2007

    Find in the library

    Mandatory

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