|
Lecturer(s)
|
-
Šmilauer Petr, doc. RNDr. Ph.D.
|
|
Course content
|
Content of lectures: Introduction to S language and R software (2 weeks), summarizing and extending linear models including ANOVA (3 weeks), generalized linear models (3 weeks), generalized additive models, classification and regression trees, survival analysis, linear mixed-effect models, the choice and combinations of regression models Content of practicals: Complements the lectures, not temporaly separated, but rather inter-mixed with the lectures. Students also submit 5-6 homework tasks, scored by the lecturer.
|
|
Learning activities and teaching methods
|
Monologic (reading, lecture, briefing), Demonstration, Projection, E-learning
- Class attendance
- 56 hours per semester
- Preparation for classes
- 42 hours per semester
- Preparation for credit
- 32 hours per semester
- Preparation for exam
- 16 hours per semester
|
|
Learning outcomes
|
Students will learn about more advanced methods of statistical modelling and how to use them correctly. In this course I stress the complementarity of testing hypotheses and graphical exploration of data. I focus on the generalized linear models (GLM), but the other model types are also presented, whether they extend GLMs (loess, GAM, GLMM) or not (classification and regression trees, spatial processes analysis).
The students learn to: (1) work with R software on the Microsoft Windows platform, including data import from a table processor (e.g. MS Excel) (2) choose and use linear or generalized linear models for data analysis and perform regression diagnostic for fitted models (3) choose an appropriate method of the event analysis in time, typically as a survival analysis (4) properly apply the methods of regression or classification trees, depending on the type of response variable, and also to perform cross-validation to determine optimum tree size (5) work with the simpler types of linear and non-linear models with mixed effects
|
|
Prerequisites
|
Students already understand the principles of testing statistical hypotheses and know the basic types of linear models (including ANOVA models)
|
|
Assessment methods and criteria
|
Written examination, Test, Interim evaluation
Students should study the materials provided by lecturer before the corresponding topic is discussed in the lecture - practicals. Students are provided with homework tasks (5-7 times) during the term, and their solutions are scored by the lecturer.
|
|
Recommended literature
|
-
Faraway (2006): Extending the Linear Model with R. Chapman & Hall/CRC, London.
-
J. Fox (2008): Applied regression analysis and generalized linear models. Sage.
-
J.M. Chambers & T.J. Hastie (1992, eds): Statistical models in S. Wadsworth.
-
McCullagh & Nelder (1989): Generalized Linear Models. Chapman & Hall, London.
-
T.J. Hastie & R. Tibshirani (1990): Generalized additive models. Chapman & Hall.
|