PřF:M6120 Linear statistical models II - Course Information
M6120 Linear statistical models II
Faculty of ScienceSpring 2020
- Extent and Intensity
- 2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
- Teacher(s)
- doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Vojtěch Šindlář (seminar tutor) - Guaranteed by
- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Wed 8:00–9:50 M2,01021
- Timetable of Seminar Groups:
M6120/02: Mon 18:00–19:50 MP1,01014, V. Šindlář - Prerequisites
- M5120 Linear Models in Statistics I
Linear regression model: at the level of the course M5120. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Biomedical bioinformatics (programme PřF, N-MBB)
- Epidemiology and modeling (programme PřF, N-MBB)
- Course objectives
- In the first part, the course offers a detailed study of selected special cases of the linear model. In the second part, an overview of a broad spectrum of generalizations of the linear model is provided; the focus is on the applications of these methods and connections between them. The students are also made aware of the master courses and literature offering more detailed information on the studied modelling techniques.
- Learning outcomes
- Student will be able:
- to test statistical hypotheses in LRM;
- to build up and explain suitable LRM;
- to apply LRM on real data;
- to implement LRM in R. - Syllabus
- Asymptotic statistical tests as Linear Regression Mode (LRM)
- One-way analysis of variance (ANOVA) with fixed effects (homogeneity and inhomogeneity of variances)
- Two-way and hierarchical ANOVA with fixed effects.
- Analysis of covariance (ANCOVA).
- Quadratic and polynomial LRM.
- Joint and conditional multivariate normal disrtribution.
- Correlation analysis.
- LRM (homogeneity and inhomogeneity of variances), LRM with fixed effects and correlated errors, weighted least squares.
- Orthogonal regression model.
- Implementations in R.
- Literature
- recommended literature
- KATINA, Stanislav, Miroslav KRÁLÍK and Adéla HUPKOVÁ. Aplikovaná štatistická inferencia I. Biologická antropológia očami matematickej štatistiky (Applied statistical inference I). 1. vyd. Brno: Masarykova univerzita, 2015, 320 pp. ISBN 978-80-210-7752-2. info
- HARRELL, Frank E. Regression modeling strategies : with applications to linear models, logistic and ordinal regression, and survival analysis. Second edition. Heidelberg: Springer, 2015, xxiii, 582. ISBN 9783319194240. info
- FARAWAY, Julian James. Linear models with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2014, xii, 274. ISBN 9781439887332. info
- RAO, C. Radhakrishna and Helge TOUTENBURG. Linear models : least squares and alternatives. New York: Springer-Verlag, 1995, 352 s. ISBN 0387945628. info
- Teaching methods
- Lectures: theoretical explanation with practical examples.
Exercises: practicals focused on data analysis in R. - Assessment methods
- Conditions: semestral project (homework), oral final exam.
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually. - Teacher's information
- https://is.muni.cz/auth/el/1431/jaro2017/M6120/index.qwarp
- Enrolment Statistics (Spring 2020, recent)
- Permalink: https://is.muni.cz/course/sci/spring2020/M6120