Statistics and Data Science II, 7.5 credits

Autumn 2024, Half-time, Norrköping

Semester Autumn 2024
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-46102
Period 202444 - 202503
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits within one of the following areas humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences
  • 15 ECTS credits passed in one or several of the following subjects:
    Statistics
    Mathematics
    Computer science
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Maria Brandén, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

Statistics and Data Science II, 7.5 credits

Autumn 2025, Half-time, Norrköping

Semester Autumn 2025
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-46102
Period 202544 - 202603
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits within one of the following areas humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences
  • 15 ECTS credits passed in one or several of the following subjects:
    Statistics
    Mathematics
    Computer science
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Maria Brandén, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

This course introduces the principles and practice of linear regression modeling. Underlying model assumptions are reviewed and scrutinized. In intensive computer laboratories, statistical tools for creating appropriate data structures and estimating models using real data are presented and guidance is provided in interpretation of model parameters. The remainder of the course focuses on causal inference and the potential outcomes framework. Panel data models and statistical tools for their estimation are presented, and their potential to improve causal inference are compared. Discussion is extended to consider natural experiments and instrumental variable approaches to causal inference. The sensitivity of estimates to violations of model assumptions are evaluated, with special attention given to methods centering on computer simulation.