Syllabus for Categorical Data Analysis
Analys av kategoridata
- 7.5 credits
- Course code: 2ST121
- 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: 2019-03-08
- Established by: The Department Board
- Revised: 2021-10-15
- Revised by: The Department Board
- Applies from: Autumn 2022
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Entry requirements:
120 credits including 90 credits in statistics.
- Responsible department: Department of Statistics
Learning outcomes
A student who has taken this course will:
- become familiar with software used in analysing categorical data
- be able to apply techniques to analyse different kinds of two-way contingency tables
- master the theory and application of the logistic regression model for analysing discrete response variable models
- master the theory and application of the loglinear model for analysing multidimensional contingency tables
- be able to carry out analysis and interpretation of loglinear models using the conditional independence graph and the generator multigraph
Content
- Two-way tables-theory, sampling schemes, and inference
- Logistic regression model-theory and application
- Loglinear model-theory and application
- Conditional independence graph-construction, application, and interpretation
- Generator multigraph-construction, application, and interpretation
Instruction
Instruction is given in the form of in-class lectures, computer exercises, and seminars.
Assessment
The examination takes place through a written examination and/or through written and/or oral presentation of take-home assignments.
Other directives
This course is part of the master degree program in statistics.
Reading list
The reading list is missing. For further information, please contact the responsible department.