Linear Models: Regression analysis

Knowledge

• understand that models fit systematic data patterns but that residual random and/or systematic data patterns remain
• distinguish between a naïve experiment-inspired interpretation and a proper non-causal interpretation of model parameters
• understand that model-based inferences are affected by sampling variation and by the extent that model assumptions hold for the data

Skills

• fit linear models to data in statistical software
• interpret model parameters and related statistics in light of the underlying data and study design
• hold a model-data dialogue using diagnostics to check the model and its inferential robustness
• write up the results of an analysis in an appropriate way

Competence

• demonstrate a facility with linear modeling to answer well-defined research questions
• interpret published scientific research that uses these models and methods
• evaluate the tenability of associated inferences and knowledge claims

Compulsory course in the Master's Programme in Assessment, Measurement and Evaluation

All students enrolled in the Master's Programme in Assessment, Measurement and Evaluation have equal access to the course. Qualified exchange students or students from other master's programmes at UiO may be considered based on capacity.

Contact us if you want to apply for the course. If you are unsure of whether or not you have sufficient prior knowledge, please send us documentation of previous relevant courses you have taken.

PhD candidates can apply to the PhD version of the course: UV9218 Linear Models

This course combines lectures and computer labs with data analysis tasks in statistical software environments.

Obligatory course components:

• Attendance in the first lecture
• 80% attendance requirement for the lectures
• Attendance in the seminar
• Assignment must be passed

The exam is a written take-home assignment that asks for a concise yet accurate report of a data-analysis on a custom dataset using the strategies and model framework taught in the course.

Maximum length of this report is 1500 words (approx. 6 pages, double-spaced font size 12pt) not including references, tables and figures.

You need to have successfully fulfilled the obligatory course components in order to be allowed to sit the exam.

Previously given exams and grading guides

• Illness at exams / postponed exams
• Resitting an examination