Syllabus for Generalised Linear Models
Generaliserade linjära modeller
Syllabus
- 7.5 credits
- Course code: 2ST075
- Education cycle: Second cycle
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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: Autumn 2015
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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: Autumn 2015
Some titles may be available electronically through the University library.
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Agresti, Alan
Foundations of linear and generalized linear models
Hoboken, New Jersey: John Wiley & Sons, 2015