M6120 Linear Models in Statistics II

Faculty of Science
Spring 2017
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. Andrea Kraus, M.Sc., Ph.D. (seminar tutor)
Mgr. Markéta Janošová (assistant)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 20. 2. to Mon 22. 5. Wed 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 20. 2. to Mon 22. 5. Mon 10:00–11:50 MP1,01014, A. Kraus
M6120/02: Mon 20. 2. to Mon 22. 5. Mon 12:00–13:50 MP1,01014, A. Kraus
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 offered to students of any study field.
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.
Syllabus
  • One-way ANOVA model with fixed effects with homogenous and nonhomogeneous variances.
  • Two-way ANOVA model with fixed effects without and with interaction.
  • Special linear regression models (LRM) – regression line, analysis of covariance (ANCOVA), several regression lines, quadratic model, polynomial regression.
  • Joint and conditional multivariate normal distribution, correlation analysis – multiple, partial correlation.
  • Orthogonal LRM (Deming regression, quality control), model of calibration.
  • LRM with homogenous and nonhomogeneous variances, LRM with fixed effects and correlated errors, WLRM.
  • LRM with mixed effects (MELRM).
  • Generalised LRM.
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
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, ix, 301. ISBN 158488424X. URL info
  • HASTIE, Trevor, Robert TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, N.Y.: Springer, 2009, xxii, 745. ISBN 9780387848570. info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis
Assessment methods
Conditions: semestral data project, oral final exam.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/1431/jaro2017/M6120/index.qwarp
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2017, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2017/M6120