PřF:M5120 Linear Models in Statistics I - Course Information
M5120 Linear Models in Statistics I
Faculty of Scienceautumn 2017
- Extent and Intensity
- 2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
- Teacher(s)
- Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Mgr. Markéta Janošová (assistant) - 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
- Mon 18. 9. to Fri 15. 12. Fri 12:00–13:50 M1,01017
- Timetable of Seminar Groups:
M5120/02: Mon 18. 9. to Fri 15. 12. Wed 8:00–8:50 MP1,01014, A. Kraus - Prerequisites
- KREDITY_MIN(30) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Calculus, linear algebra. 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 offered to students of any study field.
- Course objectives
- The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
- Learning outcomes
- After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure. - Syllabus
- Problem statement.
- Descriptive statistics and graphical diagnostics.
- Projection, conditional expectation, normal distribution.
- Linear model without the assumption of normality.
- Linear model with the assumption of normality.
- Submodel.
- Residuals and model diagnostics.
- Multicollinearity and rank-defficient models.
- Practical aspects, troubleshooting.
- Literature
- recommended literature
- WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
- FARAWAY, Julian James. Linear models with R. Boca Raton: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. info
- ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
- ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
- not specified
- Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
- ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
- RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
- Teaching methods
- Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis - Assessment methods
- Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
- Language of instruction
- Czech
- Follow-Up Courses
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually. - Listed among pre-requisites of other courses
- Teacher's information
- https://is.muni.cz/el/1431/podzim2017/M5120/index.qwarp
- Enrolment Statistics (autumn 2017, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2017/M5120