Course: Linear Models
Course type: compulsory
Lecturer: Katarina Košmelj, Ph.D., Full Professor
| Study programme and level | Study field | Academic year | Semester |
|---|---|---|---|
| Applied statistics, second level | All modules | 1st | 1st |
For the timeline see Curriculum.
Prerequisites:
- Enrolment into the first year of the programme.
- Prerequisites to the written exam are the successfully completed homeworks.
Content (Syllabus outline):
Simple linear regression:
- Assumptions, parameter estimation, statistical inference. Analysis of variance and regression. Regression through the origin.
- Diagnostics: analysis of residuals, analysis of special points.
- Useful transformations.
Correlation:
- The difference between regression and correlation model, different correlation coefficients.
Multiple regression:
- parameter estimation, statistical inference;
- diagnostics, multicollinearity;
- descriptive variables in the model, model with multiple regression lines;
- polinomial model;
- complex linear models.
- Nonlinear models.
- Modelling of covariance structure (gls models).
- Linear mixed models.
Objectives and competences:
Linear models are basic statistical tool. The goals of the course are: understanding of the theory, its use in the analysis of real data, analysis of real data and interpretation of the results.
Intended learning outcomes:
Students acquire the knowledge for the independent work in the field of statistical modeling. This ability enables an upgrade to the different fields of scientific, research and expert work.
