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Course content

This course provides an introduction to principles, terminology, and strategies for statistical modelling with the linear model as initial framework for data analysis.  

The linear model is a modelling workhorse for data analyses commonly referred to in the social and behavioural sciences as regression analysis and is an essential building block towards more advanced regression-based model techniques such as multilevel analysis and structural equation modelling. 
The emphasis in the course is on understanding the logic behind the modelling techniques and getting a hold of a proper nonnaive interpretation of the model results. 
The following topics are covered in class:

1. Simple regression: 1 predictor

2. Multiple regression: more predictors

3. Mini case studies

4. Model assumptions

5. Influential Outliers

6. Categorical predictors

7. Second-order predictors: interaction 
Although the course is in se platform/software independent, we will advance the use of the open-source statistical and graphic environment R during the computer labs

Learning outcome


  • 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  


  • 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


  • 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 part of the master's programme in Assessment, Measurement and Evaluation

Exchange students or students from other master's programmes at the University of Oslo may be offered a place in the course if there are places available. External students should consider whether or not they have sufficient prior knowledge (see "recommended previous knowledge" below). Contact if you want to apply for a place in the course. If you are unsure of whether or not you have sufficient prior knowledge, please let us know about your background and previous courses you have taken.

Important: in the autumn of 2021, we cannot give admission to students from other programmes due to limited space in the teaching rooms.

Ph.d. candidates can apply to the Ph.d version of the course: UV9218 Linear Models


Recommended previous knowledge

It is recommended to have had an introductory class covering descriptive (e.g., mean, variance, correlation) and inferential statistics (e.g., hypothesis tests) such as for instance MAE4000 Data Science


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

Obligatory course components: 80% attendance requirement for the lectures and one assignment.


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.


Previous exams/grading guidelines

Submit assignments in Inspera

You submit your assignment in the digital examination system Inspera. Read about how to submit your assignment.

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

Examination support material

No examination support material is allowed.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

Resit an examination

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.


In accordance with the UiO quality assurance system, the course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Periodic evaluation autumn 2020

Facts about this course






Every autumn


Every autumn

Teaching language