PřF:M6120 Linear statistical models II - Course Information

M6120 Linear statistical models II

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

Mon 1. 3. to Fri 14. 5. Tue 8:00–9:50 online_M2

• Timetable of Seminar Groups:

M6120/01: Mon 1. 3. to Fri 14. 5. Wed 16:00–17:50 online_MP1, V. Šindlář
M6120/02: Mon 1. 3. to Fri 14. 5. Wed 18:00–19:50 online_MP1, 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. On-line using MS Teams or full-time according to the according to the development of the epidemiological situation and the applicable restrictions.

Assessment methods

Conditions: semestral data project, oral final exam. The conditions may be specified according to the development of the epidemiological situation and the applicable restrictions.

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
The lectures will take place online at MS Teams at the time of the normal lectures according to the schedule. Due to the possible low signal quality, I recommend students not to use the camera. Questions during the lecture will not be possible to ask by voice, but by chat.

The recording from the lecture will be uploaded in the IS sequentially and not in advance, so the recording will be uploaded only after the given lecture and before the next lecture. The recording does not have to contain a complete lecture, it is up to a teacher what to share from the record and share it with the students. What is a lecture recording? It can be a PDF of text written by the lecturer on the screen with an electronic pen during the lecture, and this can be supplemented by the voice (or voice and video) of the lecturer. Slides in PDF with TeX-ed text will always be available in the IS and will be shared only after the given lecture and before the next lecture.

Consultations about the lectures will take place through a discussion forum, where the lecturer / instructor moderates this discussion and new discussion forums established by students will not be taken into account. Discussion forums will be based on individual lectures and practicals (if the course has practicals) and about homework. Discussions by e-mail will not take place.

To obtain the credit, active participation in seminars is required (2 unexcused absences are allowed). An excused absence is considered exclusively an absence excused at the study department and uploaded into the information system in due time (within 5 working days from the date of the course). This is in accordance with the study regulations, where Article 9 paragraph (7) states that (7) The student is obliged to apologize in writing to the study department of the faculty within 5 working days from the date of the course being excused.

• Enrolment Statistics (Spring 2021, recent)
  
• Permalink: https://is.muni.cz/course/sci/spring2021/M6120