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.

Reading list

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