Syllabus for Generalised Linear Models

Generaliserade linjära modeller

Syllabus

  • 7.5 credits
  • Course code: 2ST075
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1N

    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: 2007-01-24
  • Established by: The Faculty Board of Social Sciences
  • Revised: 2014-10-23
  • Revised by: The Department Board
  • Applies from: week 36, 2015
  • Entry requirements: 120 credits including 90 credits in statistics.
  • Responsible department: Department of Statistics

Learning outcomes

After completing the course the student is expected to

  • have an overview of the models that belong to the class of generalised linear models
  • be able to use the most common of these models in statistical data analysis in medical and other applications
  • be able to determine which model is the most appropriate in different applications
  • be able to assimilate the content of scientific articles concerning generalised linear models
  • have the ability to both orally and in written form account for results of analyses based on generalised linear models.

Content

Overview of linear statistical models. Generalised linear models: likelihood-based inference. Models with different link-functions and distributions, such as models for discrete data; binary regression; analysis of contingency tables. Introduction to log-linear models. Model estimation. Residual analysis. Practical examples from different application areas.

Instruction

Instruction is given in form of lectures.

Assessment

The examination takes place partly through a written examination at the end of the course and/or through compulsory written and oral assignments.

Reading list

Reading list

Applies from: week 36, 2015

Some titles may be available electronically through the University library.

  • Agresti, Alan Foundations of linear and generalized linear models

    Hoboken, New Jersey: John Wiley & Sons, 2015

    Find in the library