Syllabus for Categorical Data Analysis

Analys av kategoridata

  • 7.5 credits
  • Course code: 2ST121
  • 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: 2019-03-08
  • Established by: The Department Board
  • Revised: 2021-10-15
  • Revised by: The Department Board
  • Applies from: week 35, 2022
  • 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.

Syllabus Revisions

Reading list

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

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