Subject description
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Linear regression model
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Different types of explanatory variables in linear model
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Model diagnostics, special points, nonconstant variance, transformations
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Polinomial regression and splines
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Model selection
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Linear mixed models
The subject is taught in programs
Objectives and competences
Linear models are basic statistical tools. 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.
Teaching and learning methods
Lectures and lab classes, seminar. Lectures and labs are in the computer classrooms where students use the theoretical knowledge on the real data.
Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.
Expected study results
Students acquire the knowledge for the independent work in the field of statistical modelling with R programme. This ability enables an upgrade to the different fields of scientific, research and expert work.
Basic sources and literature
- Kastelec D. ,Košmelj K., Šinkovec H.: Študijsko gradivo (pdf datoteke) dostopno v e-učilnici in na usb ključku v knjižnici
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning with applications in R, https://www.statlearning.com, Springer-Verlag, New York
- Andrew Gelman Jennifer Hill Aki Vehtari (2020).Regression and Other Stories, https://avehtari.github.io/ROS-Examples/index.html, Cambridge University Press
- Harrell F. E. Jr.(2015): Regression Modeling Strategies. Springer
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana