PřF:M5120 Linear Models in Statistics I - Course Information
M5120 Linear Models in Statistics I
Faculty of ScienceAutumn 2016
- 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)
- Guaranteed by
- prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Mon 19. 9. to Sun 18. 12. Mon 8:00–9:50 M2,01021
- Timetable of Seminar Groups:
M5120/02: Mon 19. 9. to Sun 18. 12. Tue 12:00–12:50 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. After the course the students are expected to recognize the situations that can be addressed by linear models, formulate and implement the model, and interpret the results. At the same time, the students are made aware of the limitations of the model and should be able to recognize and possibly avoid problems in a given situation.
- Syllabus
- Problem statement.
- Descriptive statistics and graphical diagnostics.
- Projection, conditional expectation, normal distribution.
- Correlation.
- 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
- 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/auth/el/1431/podzim2016/M5120/index.qwarp
- Enrolment Statistics (Autumn 2016, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2016/M5120