M6120 Linear statistical models II

Faculty of Science
Spring 2025
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
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
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
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)
The course is taught annually.
The course is taught: every week.
Teacher's information
https://is.muni.cz/auth/el/1431/jaro2017/M6120/index.qwarp
Přednášky budou probíhat prezenčně dle rozvrhu. V IS bude vždy k dispozici záznam textu přednášky v PDF (přednášející text píše elektronickým perem na obrazovce tabletu a tento se zobrazuje na plátně) a slajdy v PDF s TeXovaným textem. Záznamy se budou sdílet až po dané přednášce a před další přednáškou.

K získání zápočtu je potřeba aktivní účast na cvičeních (povolené jsou 2 neomluvené absence). Za omluvenou absenci se považuje výhradně absence omluvená na studijním oddělení a zavedená do informačního systému v řádném termínu (do 5 pracovních dnů od termínu konání výuky). Je to v souladu se studijním řádem, kde se v čl.9 odstavci (7) píše, že (7) Student je povinen písemně omluvit na studijním oddělení fakulty svou neúčast do 5 pracovních dnů od termínu konání výuky, jež je omlouvána.

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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

M6120 Linear statistical models II

Faculty of Science
Spring 2024
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 19. 2. to Sun 26. 5. Tue 8:00–9:50 M4,01024
  • Timetable of Seminar Groups:
M6120/01: Mon 19. 2. to Sun 26. 5. Thu 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
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
Přednášky budou probíhat prezenčně dle rozvrhu. V IS bude vždy k dispozici záznam textu přednášky v PDF (přednášející text píše elektronickým perem na obrazovce tabletu a tento se zobrazuje na plátně) a slajdy v PDF s TeXovaným textem. Záznamy se budou sdílet až po dané přednášce a před další přednáškou.

K získání zápočtu je potřeba aktivní účast na cvičeních (povolené jsou 2 neomluvené absence). Za omluvenou absenci se považuje výhradně absence omluvená na studijním oddělení a zavedená do informačního systému v řádném termínu (do 5 pracovních dnů od termínu konání výuky). Je to v souladu se studijním řádem, kde se v čl.9 odstavci (7) píše, že (7) Student je povinen písemně omluvit na studijním oddělení fakulty svou neúčast do 5 pracovních dnů od termínu konání výuky, jež je omlouvána.

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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.

M6120 Linear statistical models II

Faculty of Science
Spring 2023
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 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Tue 18:00–19:50 MP1,01014, V. Šindlář
M6120/02: Mon 8:00–9:50 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
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
Přednášky budou probíhat prezenčně dle rozvrhu. V IS bude vždy k dispozici záznam textu přednášky v PDF (přednášející text píše elektronickým perem na obrazovce tabletu a tento se zobrazuje na plátně) a slajdy v PDF s TeXovaným textem. Záznamy se budou sdílet až po dané přednášce a před další přednáškou.

K získání zápočtu je potřeba aktivní účast na cvičeních (povolené jsou 2 neomluvené absence). Za omluvenou absenci se považuje výhradně absence omluvená na studijním oddělení a zavedená do informačního systému v řádném termínu (do 5 pracovních dnů od termínu konání výuky). Je to v souladu se studijním řádem, kde se v čl.9 odstavci (7) píše, že (7) Student je povinen písemně omluvit na studijním oddělení fakulty svou neúčast do 5 pracovních dnů od termínu konání výuky, jež je omlouvána.

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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2024, Spring 2025.

M6120 Linear statistical models II

Faculty of Science
Spring 2022
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
Tue 10:00–11:50 M4,01024
  • Timetable of Seminar Groups:
M6120/01: Wed 10:00–11:50 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
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
Přednášky budou probíhat prezenčně dle rozvrhu. V IS bude vždy k dispozici záznam textu přednášky v PDF (přednášející text píše elektronickým perem na obrazovce tabletu a tento se zobrazuje na plátně) a slajdy v PDF s TeXovaným textem. Záznamy se budou sdílet až po dané přednášce a před další přednáškou.

K získání zápočtu je potřeba aktivní účast na cvičeních (povolené jsou 2 neomluvené absence). Za omluvenou absenci se považuje výhradně absence omluvená na studijním oddělení a zavedená do informačního systému v řádném termínu (do 5 pracovních dnů od termínu konání výuky). Je to v souladu se studijním řádem, kde se v čl.9 odstavci (7) píše, že (7) Student je povinen písemně omluvit na studijním oddělení fakulty svou neúčast do 5 pracovních dnů od termínu konání výuky, jež je omlouvána.

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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear statistical models II

Faculty of Science
Spring 2021
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
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.

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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear statistical models II

Faculty of Science
Spring 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:
M6120/01: Mon 16:00–17:50 MP1,01014, V. Šindlář
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
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
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 2017, spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2019
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 18. 2. to Fri 17. 5. Wed 8:00–9:50 M2,01021
  • Timetable of Seminar Groups:
M6120/01: Mon 18. 2. to Fri 17. 5. Mon 14:00–15:50 MP1,01014, V. Šindlář
M6120/02: Mon 18. 2. to Fri 17. 5. Tue 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 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.
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: 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 2017, spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
spring 2018
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 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Fri 8:00–9:50 MP1,01014, V. Šindlář
M6120/02: Thu 17:00–18:50 MP2,01014a, 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 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.
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: 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 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

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.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2016
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Andrea Kraus, M.Sc., Ph.D. (seminar tutor)
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
Tue 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Tue 12:00–13:50 MP1,01014, A. Kraus
M6120/02: Thu 8:00–9:50 MP1,01014, A. Kraus
Prerequisites
M5120 Linear Models in Statistics I
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis. Computer skill: working knowledge of the numerical computing environment R.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the theory of linear models and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in the theory of linear models;
to apply acquired pieces of knowledge for the solution of specific problems of linear models that are not full rank, especially the analysis of variance including problems of applicative character.
For statistical calculations, students learn during the seminars to use the programming environment R in detail which then, they will be able to use in practice.
Syllabus
  • Regular (full rank) and singular (not of full rank) linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification. Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Participation in seminars (10%), four homework assigments (30%), final oral exam (60%).
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2015
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Kateřina Pokorová, Ph.D. (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 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 12:00–13:50 MP1,01014, M. Forbelská
M6120/02: Wed 8:00–9:50 MP1,01014, M. Forbelská
M6120/03: Tue 12:00–13:50 MP1,01014, M. Forbelská
Prerequisites
M5120 Linear Models in Statistics I
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis. Computer skill: working knowledge of the numerical computing environment R.
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
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the theory of linear models and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in the theory of linear models;
to apply acquired pieces of knowledge for the solution of specific problems of linear models that are not full rank, especially the analysis of variance including problems of applicative character.
For statistical calculations, students learn during the seminars to use the programming environment R in detail which then, they will be able to use in practice.
Syllabus
  • Regular (full rank) and singular (not of full rank) linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification. Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Participation in seminars (10%), four homework assigments (30%), final oral exam (60%).
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2014
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Marie Leváková, Ph.D. (seminar tutor)
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 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 10:00–11:50 MP1,01014, M. Forbelská
M6120/02: Fri 12:00–13:50 MP1,01014, M. Forbelská
M6120/03: Thu 18:00–19:50 MP1,01014, M. Leváková
Prerequisites
M5120 Linear Models in Statistics I
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis. Computer skill: working knowledge of the numerical computing environment R.
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
Course objectives
After passing the course, the student will be able:
to define and interpret the basic notions used in the theory of linear models and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in the theory of linear models;
to apply acquired pieces of knowledge for the solution of specific problems of linear models that are not full rank, especially the analysis of variance including problems of applicative character.
For statistical calculations, students learn during the seminars to use the programming environment R in detail which then, they will be able to use in practice.
Syllabus
  • Regular (full rank) and singular (not of full rank) linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification. Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Participation in seminars (10%), four homework assigments (30%), final oral exam (60%).
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2013
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Marie Leváková, Ph.D. (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
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
Tue 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Fri 8:00–9:50 MP1,01014, M. Forbelská
M6120/02: Tue 8:00–9:50 MP1,01014, M. Forbelská
M6120/03: Tue 18:00–19:50 MP1,01014, M. Leváková
M6120/04: Tue 16:00–17:50 MP1,01014, M. Leváková
M6120/05: Mon 16:00–17:50 MP1,01014, O. Pokora
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2012
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Marie Leváková, Ph.D. (seminar tutor)
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
Fri 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 8:00–9:50 MP2,01014a, Mon 8:00–9:50 MP1,01014, M. Forbelská
M6120/02: Tue 10:00–11:50 MP1,01014, M. Leváková
M6120/03: Mon 16:00–17:50 MP1,01014, M. Leváková
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2011
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)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Fri 10:00–11:50 MP1,01014, M. Forbelská, O. Pokora
M6120/02: Fri 8:00–9:50 MP2,01014a, M. Forbelská, O. Pokora
M6120/03: Fri 8:00–9:50 MP1,01014, M. Forbelská, O. Pokora
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2010
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Pavla Krajíčková, Ph.D. (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 9:00–10:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Mon 11:00–12:50 MP1,01014, O. Pokora
M6120/02: Mon 9:00–10:50 MP1,01014, O. Pokora
M6120/03: Wed 12:00–13:50 MP1,01014, P. Krajíčková
M6120/04: Thu 17:00–18:50 MP1,01014, P. Krajíčková
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2009
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. Pavla Krajíčková, Ph.D. (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 11:00–12:50 M1,01017
  • Timetable of Seminar Groups:
M6120/01: Thu 8:00–9:50 MP1,01014, O. Pokora
M6120/02: Wed 17:00–18:50 MP1,01014, P. Krajíčková
M6120/03: Tue 17:00–18:50 MP1,01014, P. Krajíčková
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2008
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
doc. Mgr. Kamila Hasilová, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 8:00–9:50 UP1
  • Timetable of Seminar Groups:
M6120/01: Mon 11:00–12:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
M6120/02: Thu 18:00–19:50 M3,04005 - dříve Janáčkovo nám. 2a, K. Hasilová
M6120/03: Tue 17:00–18:50 M3,04005 - dříve Janáčkovo nám. 2a, K. Hasilová
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives (in Czech)
Kurz je zaměřen na testy dobré zhody, kontingenční tabulky, na lineární modely, které nejsou plné hodnosti a na analýzu rozptylu. Vyklad je důsledně založen na maticovém přístupu. Jde o kurz, jehož praktické využití v dalších oborech je velmi časté.
Syllabus (in Czech)
  • Testy dobré shody. Multinomické rozdělení. Testy dobré shody při známych a neznámych parametrech. Kontingenční tabulky. Test nezávislosti v kontingenčních tabulkách. Fischerův faktoriálový test. Lineární model s plnou a neúplnou hodností. Testy hypotéz v modelu s neúplnou hodností. Testování submodelů. Analýza rozptylu. Jednoduché třídění. Dvojné třídění.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
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 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2007
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 14:00–15:50 N21
  • Timetable of Seminar Groups:
M6120/01: Thu 11:00–12:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
M6120/02: Wed 18:00–19:50 M3,04005 - dříve Janáčkovo nám. 2a, O. Pokora
M6120/03: Wed 16:00–17:50 M3,04005 - dříve Janáčkovo nám. 2a, O. Pokora
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives (in Czech)
Kurz je zaměřen na testy dobré zhody, kontingenční tabulky, na lineární modely, které nejsou plné hodnosti a na analýzu rozptylu. Vyklad je důsledně založen na maticovém přístupu. Jde o kurz, jehož praktické využití v dalších oborech je velmi časté.
Syllabus (in Czech)
  • Testy dobré shody. Multinomické rozdělení. Testy dobré shody při známych a neznámych parametrech. Kontingenční tabulky. Test nezávislosti v kontingenčních tabulkách. Fischerův faktoriálový test. Lineární model s plnou a neúplnou hodností. Testy hypotéz v modelu s neúplnou hodností. Testování submodelů. Analýza rozptylu. Jednoduché třídění. Dvojné třídění.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
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 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2006
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Marie Forbelská, Ph.D.
Timetable
Wed 14:00–15:50 N41
  • Timetable of Seminar Groups:
M6120/01: Tue 7:00–8:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
M6120/02: Mon 10:00–11:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives (in Czech)
Kurz je zaměřen na testy dobré zhody, kontingenční tabulky, na lineární modely, které nejsou plné hodnosti a na analýzu rozptylu. Vyklad je důsledně založen na maticovém přístupu. Jde o kurz, jehož praktické využití v dalších oborech je velmi časté.
Syllabus (in Czech)
  • Testy dobré shody. Multinomické rozdělení. Testy dobré shody při známych a neznámych parametrech. Kontingenční tabulky. Test nezávislosti v kontingenčních tabulkách. Fischerův faktoriálový test. Lineární model s plnou a neúplnou hodností. Testy hypotéz v modelu s neúplnou hodností. Testování submodelů. Analýza rozptylu. Jednoduché třídění. Dvojné třídění.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
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 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2005
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Štěpán Mikoláš
Timetable
Wed 8:00–9:50 UM
  • Timetable of Seminar Groups:
M6120/01: Tue 10:00–11:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská, Rozvrhobvě doporučeno:Me
M6120/02: Tue 8:00–9:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská, Rozvrhově doporučeno: M,Mo,Mf
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
there are 8 fields of study the course is directly associated with, display
Course objectives (in Czech)
Kurz je zaměřen na testy dobré zhody, kontingenční tabulky, na lineární modely, které nejsou plné hodnosti a na analýzu rozptylu. Vyklad je důsledně založen na maticovém přístupu. Jde o kurz, jehož praktické využití v dalších oborech je velmi časté.
Syllabus (in Czech)
  • Testy dobré shody. Multinomické rozdělení. Testy dobré shody při známych a neznámych parametrech. Kontingenční tabulky. Test nezávislosti v kontingenčních tabulkách. Fischerův faktoriálový test. Lineární model s plnou a neúplnou hodností. Testy hypotéz v modelu s neúplnou hodností. Testování submodelů. Analýza rozptylu. Jednoduché třídění. Dvojné třídění.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
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 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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2004
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer), RNDr. Štěpán Mikoláš (deputy)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Marie Forbelská, Ph.D.
Timetable of Seminar Groups
M6120/01: No timetable has been entered into IS. M. Forbelská
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
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 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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2003
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc.
Timetable of Seminar Groups
M6120/01: No timetable has been entered into IS. M. Forbelská
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, 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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
spring 2012 - acreditation

The information about the term spring 2012 - acreditation is not made public

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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
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
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
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 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2011 - only for the accreditation
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)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives
The statistical course is focused on googness-of-fit tests, contingency tables, possibly singular linear regression models, analysis of variance. The explanation is fully based on matrix access. The practical applications of the course is very frequent in many branches.
Syllabus
  • Goodness-of-fit tests. Discrete multinomial distribution. Goodness-of-fit tests with unknown parameters. Contingency tables. Testing independency in contingency tables. Fisher's factorial test. Regular and singular linear model. Testing hypotheses in singular linear model. Testing of submodels. Analysis of variance. One-way classification. Two-way classification.
Literature
  • 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: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
lecture, class exercises; 2 written tests; final grade: written and oral examination
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of 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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6120 Linear Models in Statistics II

Faculty of Science
Spring 2008 - for the purpose of the accreditation
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Gejza Wimmer, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Gejza Wimmer, DrSc.
Prerequisites (in Czech)
M5120 Linear Models in Statistics I
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
Course objectives (in Czech)
Kurz je zaměřen na testy dobré zhody, kontingenční tabulky, na lineární modely, které nejsou plné hodnosti a na analýzu rozptylu. Vyklad je důsledně založen na maticovém přístupu. Jde o kurz, jehož praktické využití v dalších oborech je velmi časté.
Syllabus (in Czech)
  • Testy dobré shody. Multinomické rozdělení. Testy dobré shody při známych a neznámych parametrech. Kontingenční tabulky. Test nezávislosti v kontingenčních tabulkách. Fischerův faktoriálový test. Lineární model s plnou a neúplnou hodností. Testy hypotéz v modelu s neúplnou hodností. Testování submodelů. Analýza rozptylu. Jednoduché třídění. Dvojné třídění.
Literature
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms 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 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)