MAE4001 – Linear Models: Regression analysis
Course description
Schedule, syllabus and examination date
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 non-naive 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
Knowledge
- understand that models fit systematic data patterns but that residual random and/or systematic data patterns remain
- distinguish between a na?ve 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
Admission to the course
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
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.
Overlapping courses
- 3 credits overlap with UV9218 – Linear Models.
Teaching
This course combines lectures and computer labs with data analysis tasks in statistical software environments.
Obligatory course components:
- 80% attendance requirement for the lectures?
- Assignment must be passed
Examination
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
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.
Resit an examination
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.