M5120 Linear Models in Statistics I

Faculty of Science
Autumn 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).
Taught in person.
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Bc. Michaela Marčeková (seminar tutor)
Bc. Tomáš Pompa (seminar tutor)
Guaranteed by
doc. Mgr. David Kraus, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • FARAWAY, Julian James. Linear models with R. Second edition. Boca Raton: CRC Press, 2015, 1 online. ISBN 9781439887349. URL info
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2023/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 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).
Taught in person.
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Bc. Michaela Marčeková (seminar tutor)
Bc. Tomáš Pompa (seminar tutor)
Guaranteed by
doc. Mgr. David Kraus, Ph.D.
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 M4,01024
  • Timetable of Seminar Groups:
M5120/01: Tue 16:00–17:50 MP1,01014, T. Pompa
M5120/02: Tue 18:00–19:50 MP1,01014, M. Marčeková
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • FARAWAY, Julian James. Linear models with R. Second edition. Boca Raton: CRC Press, 2015, 1 online. ISBN 9781439887349. URL info
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2023/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 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).
Taught in person.
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Mgr. Jan Holub (seminar tutor)
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 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 16:00–17:50 MP1,01014, V. Šindlář
M5120/02: Mon 12:00–13:50 MP1,01014, V. Šindlář
M5120/03: Fri 8:00–9:50 MP1,01014, J. Holub
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2022/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
autumn 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).
Taught in person.
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
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
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Wed 16:00–17:50 MP1,01014, A. Kraus
M5120/02: Mon 16:00–17:50 MP1,01014, A. Kraus
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam. The form of the exam will be adjusted to the epidemiological situation preceding the date of the exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2021/M5120/index.qwarp
https://is.muni.cz/auth/el/sci/podzim2021/M5120/index-WMECKq.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2020
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Taught online.
Teacher(s)
Mgr. Andrea Kraus, M.Sc., 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 16:00–17:50 M2,01021
  • Timetable of Seminar Groups:
M5120/01: Mon 14:00–14:50 MP1,01014, A. Kraus, V. Šindlář
M5120/02: Mon 15:00–15:50 MP1,01014, A. Kraus, V. Šindlář
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Conditions: semestral data project, written and oral final exam, potentially with a bonus for an optional written an oral midterm exam. The form of the exam will be adjusted to the epidemiological situation preceding the date of the exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2020/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2019
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Mgr. 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
Thu 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Tue 19:00–19:50 MP1,01014, V. Šindlář
M5120/02: Tue 18:00–18:50 MP1,01014, V. Šindlář
Prerequisites
M4122 Probability and Statistics II
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2019/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2018
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Mgr. 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 17. 9. to Fri 14. 12. Fri 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 17. 9. to Fri 14. 12. Tue 13:00–13:50 MP1,01014, V. Šindlář
M5120/02: Mon 17. 9. to Fri 14. 12. Tue 12:00–12:50 MP1,01014, V. Šindlář
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection.
  • Normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/el/1431/podzim2018/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
autumn 2017
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Mgr. Markéta Janošová (assistant)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 18. 9. to Fri 15. 12. Fri 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 18. 9. to Fri 15. 12. Wed 9:00–9:50 MP1,01014, A. Kraus
M5120/02: Mon 18. 9. to Fri 15. 12. Wed 8:00–8:50 MP1,01014, A. Kraus
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by linear models;
- are able to formulate and implement a model, and interpret the results;
- are aware of the limitations of the model;
- in a given situation, they are able to anticipate possible problems and avoid them by slightly modifying the procedure.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/el/1431/podzim2017/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2016
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 19. 9. to Sun 18. 12. Mon 8:00–9:50 M2,01021
  • Timetable of Seminar Groups:
M5120/01: Mon 19. 9. to Sun 18. 12. Tue 11:00–11:50 MP1,01014, A. Kraus
M5120/02: Mon 19. 9. to Sun 18. 12. Tue 12:00–12:50 MP1,01014, A. Kraus
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Calculus, linear algebra. Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers an overview of linear statistical models as the fundamental tool of statistical analysis. Within one semester, the students encounter theory, software implementation, applications and interpretation. After the course the students are expected to recognize the situations that can be addressed by linear models, formulate and implement the model, and interpret the results. At the same time, the students are made aware of the limitations of the model and should be able to recognize and possibly avoid problems in a given situation.
Syllabus
  • Problem statement.
  • Descriptive statistics and graphical diagnostics.
  • Projection, conditional expectation, normal distribution.
  • Correlation.
  • Linear model without the assumption of normality.
  • Linear model with the assumption of normality.
  • Submodel.
  • Residuals and model diagnostics.
  • Multicollinearity and rank-defficient models.
  • Practical aspects, troubleshooting.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006, xvii, 392. ISBN 1584884746. info
  • FARAWAY, Julian James. Linear models with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2005, x, 229. ISBN 1584884258. URL info
  • ZVÁRA, Karel. Regrese (Regression). Praha, 2008, 253 pp. ISBN 978-80-7378-041-8. info
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
    not specified
  • Applied multivariate statistical analysis. Edited by Richard Arnold Johnson - Dean W. Wichern. 6th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2007, xviii, 773. ISBN 9780131877153. info
  • ANDĚL, Jiří. Matematická statistika. Vyd. 2. Praha: SNTL - nakladatelství technické literatury, Alfa, vydavatelstvo technickej a ekonomickej literatury, 1985, 346 s. URL info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. 1. vyd. Praha: Academia, 1978, 666 s. URL info
Teaching methods
Lectures: theoretical explanation with practical examples.
Exercises: exercises focused on data analysis
Assessment methods
Conditions: semestral data project, written final exam, potentially with a bonus for an optional written midterm exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/1431/podzim2016/M5120/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2015
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Wed 14:00–14:50 MP1,01014, O. Pokora
M5120/02: Wed 13:00–13:50 MP1,01014, O. Pokora
M5120/03: Wed 15:00–15:50 MP1,01014, O. Pokora
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Basics of probability and statistics, theory of estimation, testing statistical hypotheses. Calculus and linear algebra. Computer exercices: basis knowledge of R language at the level of the course M4130 "Mathematical Software".
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
At the end of the course the student should be able to understand and use basic methods of statistical regression analysis, which are explained by a matrix approach. Programming environment R is used in the exercises for the basic statistical analysis.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix
  • Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms
  • Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, basic of regression diagnostics
  • Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them
  • Exercises: estimation of parameters using maximum likelihood and moment method; random vectors and matrix calculus; variance-covariance matrix; linear regression model; sample correlations; practical computations in R software.
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. 1. vyd. 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
Conditions: active participation in seminars, individual homeworks, 1 test on computer. Evaluation: written (weight 50 %) and oral (weight 50 %) final examination, at least 50 % of points is needed to pass.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2014
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Tue 12:00–12:50 MP1,01014, O. Pokora
M5120/02: Thu 16:00–16:50 MP1,01014, O. Pokora
M5120/03: Thu 17:00–17:50 MP1,01014, O. Pokora
M5120/04: Wed 17:00–17:50 MP1,01014, O. Pokora
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Základy teorie pravděpodobnosti a matematické statistiky, teorie odhadu a testování statistických hypotéz. Práce s počítačem: uživatelská znalost programového prostředí 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
At the end of the course the student should be able to understand and use basic methods of statistical regression analysis, which are explained by a matrix approach. Programming environment R is used in the exercises for the basic statistical analysis.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix
  • Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms
  • Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, basic of regression diagnostics
  • Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them
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. 1. vyd. 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
Conditions: active participation in seminars, individual homeworks, 1 test on computer. Evaluation: written (weight 50 %) and oral (weight 50 %) final examination, at least 50 % of points is needed to pass.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2013
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Fri 12:00–12:50 MP1,01014, O. Pokora
M5120/02: Fri 11:00–11:50 MP1,01014, O. Pokora
M5120/03: Fri 10:00–10:50 MP1,01014, O. Pokora
Prerequisites
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
Základy teorie pravděpodobnosti a matematické statistiky, teorie odhadu a testování statistických hypotéz. Práce s počítačem: uživatelská znalost programového prostředí 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
At the end of the course the student should be able to understand and use basic methods of statistical regression analysis, which are explained by a matrix approach. Programming environment R is used in the exercises for the basic statistical analysis.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix
  • Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms
  • Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, basic of regression diagnostics
  • Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them
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. 1. vyd. 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
Conditions: active participation in seminars, individual homeworks, 1 test on computer. Evaluation: written (weight 50 %) and oral (weight 50 %) final examination, at least 50 % of points is needed to pass.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2012
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Fri 8:00–8:50 MP1,01014, O. Pokora
M5120/02: Fri 9:00–9:50 MP1,01014, O. Pokora
M5120/03: Fri 10:00–10:50 MP1,01014, O. Pokora
M5120/04: Fri 11:00–11:50 MP1,01014, O. Pokora
M5120/05: Thu 11:00–11:50 MP1,01014, O. Pokora
Prerequisites (in Czech)
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the course in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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
homeworks, 1 test on computer; final grade: written and oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2011
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 8:00–8:50 MP1,01014, O. Pokora
M5120/02: Mon 9:00–9:50 MP1,01014, O. Pokora
M5120/03: Mon 10:00–10:50 MP1,01014, O. Pokora
Prerequisites (in Czech)
M4122 Probability and Statistics II || M6130 Computational statistics
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the course in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2010
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
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 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 17:00–17:50 M5,01013, O. Pokora
M5120/02: Mon 16:00–16:50 M5,01013, O. Pokora
Prerequisites (in Czech)
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Fundamental stat. methods )
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the course in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2009
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 11:00–12:50 M1,01017
  • Timetable of Seminar Groups:
M5120/1: Fri 12:00–12:50 MP1,01014, M. Forbelská
M5120/2: Fri 13:00–13:50 MP1,01014, M. Forbelská
M5120/3: Tue 8:00–8:50 MP1,01014, M. Forbelská
M5120/4: Tue 9:00–9:50 MP1,01014, M. Forbelská
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the corse in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2008
Extent and Intensity
2/1/0. 3 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 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M5120/01: Mon 8:00–8:50 M2,01021, O. Pokora
M5120/02: Mon 9:00–9:50 M3,01023, O. Pokora
M5120/03: Mon 10:00–10:50 M3,01023, O. Pokora
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the corse in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2007
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 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
Timetable
Thu 8:00–9:50 N21
  • Timetable of Seminar Groups:
M5120/01: Tue 9:00–9:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
M5120/02: Tue 10:00–10:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
M5120/03: Tue 8:00–8:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
Prerequisites (in Czech)
M4122 Statistics
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 lineární modely plné hodnosti. Výklad je důsledně založen na maticovém přístupu. V úvodních partiích je studováno mnohorozměrné normální rozdělení a rozdělení kvadratických forem. Potom následuje regresní analýza. Jde o kurz, jehož praktické využití v dalších oborech je bezprostřední a velmi časté.
Syllabus (in Czech)
  • Výběr z maticové algebry: positivně definitní matice, idempotentní matice, pseudoinverzní matice. Normální rozdělení: n-rozměrné normální rozdělení a jeho vlastnosti, rozdělení kvadratických forem. Regrese: model lineární regrese plné hodnosti, metoda nejmenších čtverců a odhad parametrů modelu, vlastnosti odhadů; testy hypotéz o parametrech a intervaly spolehlivosti za předpokladů normality; specální případy; test linearity regrese a porovnání 2 regresních modelů; základy regresní diagnostiky. Korelace: korelační koeficient, koeficient mnohonásobné korelace a parciální korelační koeficient; jejich výběrové protějšky a testování.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2006
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
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: RNDr. Štěpán Mikoláš
Timetable
Wed 13:00–14:50 N21
  • Timetable of Seminar Groups:
M5120/01: Wed 16:00–16:50 U1, O. Pokora
M5120/02: Wed 17:00–17:50 U1, O. Pokora
M5120/03: Wed 18:00–18:50 UP2, O. Pokora
Prerequisites (in Czech)
M4122 Statistics
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 lineární modely plné hodnosti. Výklad je důsledně založen na maticovém přístupu. V úvodních partiích je studováno mnohorozměrné normální rozdělení a rozdělení kvadratických forem. Potom následuje regresní analýza. Jde o kurz, jehož praktické využití v dalších oborech je bezprostřední a velmi časté.
Syllabus (in Czech)
  • Výběr z maticové algebry: positivně definitní matice, idempotentní matice, pseudoinverzní matice. Normální rozdělení: n-rozměrné normální rozdělení a jeho vlastnosti, rozdělení kvadratických forem. Regrese: model lineární regrese plné hodnosti, metoda nejmenších čtverců a odhad parametrů modelu, vlastnosti odhadů; testy hypotéz o parametrech a intervaly spolehlivosti za předpokladů normality; specální případy; test linearity regrese a porovnání 2 regresních modelů; základy regresní diagnostiky. Korelace: korelační koeficient, koeficient mnohonásobné korelace a parciální korelační koeficient; jejich výběrové protějšky a testování.
Language of instruction
Czech
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2005
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
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: RNDr. Štěpán Mikoláš
Timetable
Wed 14:00–15:50 UP1
  • Timetable of Seminar Groups:
M5120/01: Thu 8:00–8:50 U1, O. Pokora
M5120/02: Thu 9:00–9:50 U1, O. Pokora
Prerequisites (in Czech)
M4122 Statistics
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 lineární modely plné hodnosti. Výklad je důsledně založen na maticovém přístupu. V úvodních partiích je studováno mnohorozměrné normální rozdělení a rozdělení kvadratických forem. Potom následuje regresní analýza. Jde o kurz, jehož praktické využití v dalších oborech je bezprostřední a velmi časté.
Syllabus (in Czech)
  • Výběr z maticové algebry: positivně definitní matice, idempotentní matice, pseudoinverzní matice. Normální rozdělení: n-rozměrné normální rozdělení a jeho vlastnosti, rozdělení kvadratických forem. Regrese: model lineární regrese plné hodnosti, metoda nejmenších čtverců a odhad parametrů modelu, vlastnosti odhadů; testy hypotéz o parametrech a intervaly spolehlivosti za předpokladů normality; specální případy; test linearity regrese a porovnání 2 regresních modelů; základy regresní diagnostiky. Korelace: korelační koeficient, koeficient mnohonásobné korelace a parciální korelační koeficient; jejich výběrové protějšky a testování.
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2004
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. David Hampel, 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 12:00–13:50 U1
  • Timetable of Seminar Groups:
M5120/01: Thu 11:00–11:50 UM, D. Hampel, Rozvrhově doporučeno: 3.r. Mo
M5120/02: Wed 11:00–11:50 U1, D. Hampel, Rozvrhově doporučeno: 3.r. Me,Mf
Prerequisites (in Czech)
M4122 Statistics
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 lineární modely plné hodnosti. Výklad je důsledně založen na maticovém přístupu. V úvodních partiích je studováno mnohorozměrné normální rozdělení a rozdělení kvadratických forem. Potom následuje regresní analýza. Jde o kurz, jehož praktické využití v dalších oborech je bezprostřední a velmi časté.
Syllabus (in Czech)
  • Výběr z maticové algebry: positivně definitní matice, idempotentní matice, pseudoinverzní matice. Normální rozdělení: n-rozměrné normální rozdělení a jeho vlastnosti, rozdělení kvadratických forem. Regrese: model lineární regrese plné hodnosti, metoda nejmenších čtverců a odhad parametrů modelu, vlastnosti odhadů; testy hypotéz o parametrech a intervaly spolehlivosti za předpokladů normality; specální případy; test linearity regrese a porovnání 2 regresních modelů; základy regresní diagnostiky. Korelace: korelační koeficient, koeficient mnohonásobné korelace a parciální korelační koeficient; jejich výběrové protějšky a testování.
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2003
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer), RNDr. Štěpán Mikoláš (deputy)
Mgr. David Hampel, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Štěpán Mikoláš
Timetable of Seminar Groups
M5120/01: No timetable has been entered into IS. D. Hampel
Prerequisites (in Czech)
M4122 Statistics
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 can also be completed outside the examination period.
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2002
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc.
Prerequisites (in Czech)
M4122 Statistics
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 can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2011 - acreditation

The information about the term Autumn 2011 - acreditation is not made public

Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Computational statistics )
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the course in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Gejza Wimmer, DrSc. (lecturer)
Mgr. Ondřej Pokora, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
KREDITY_MIN ( 30 ) && ( M4122 Probability and Statistics II || M6130 Fundamental stat. methods )
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
At the end of this course, students should be able to understand and utilize basic procedures of statistical regression analysis. Introduced and explained are the multinomial normal distribution, its properties, the distribtion of quadratic forms, the regular regression model and optimal estimators of its parameters. Explanations are based on matrix access. The practical applications of the course in many baches is immediately.
Syllabus
  • Basic knowledge of matrix algebra: positive definite matrix, idempotent matrix, generalized inverse of matrix. Normal distribution: n-dimensional normal distribution and its properties, distribution of quadratic forms. Regression: regular linear regression model, least squares method and estimators of model's parameters, properties of the estimators, testing hypotheses about the parameters and confidence intervals for parameters, special cases - comparison of two regression dependencies, basic of regression diagnostics. Correlation: correlation coefficient, multiple correlation coefficient, partial correlation coefficient, their sampling opposites and tests for them.
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. 1. vyd. 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.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M5120 Linear Models in Statistics I

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/1/0. 3 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: RNDr. Marie Forbelská, Ph.D.
Prerequisites (in Czech)
M4122 Statistics
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 lineární modely plné hodnosti. Výklad je důsledně založen na maticovém přístupu. V úvodních partiích je studováno mnohorozměrné normální rozdělení a rozdělení kvadratických forem. Potom následuje regresní analýza. Jde o kurz, jehož praktické využití v dalších oborech je bezprostřední a velmi časté.
Syllabus (in Czech)
  • Výběr z maticové algebry: positivně definitní matice, idempotentní matice, pseudoinverzní matice. Normální rozdělení: n-rozměrné normální rozdělení a jeho vlastnosti, rozdělení kvadratických forem. Regrese: model lineární regrese plné hodnosti, metoda nejmenších čtverců a odhad parametrů modelu, vlastnosti odhadů; testy hypotéz o parametrech a intervaly spolehlivosti za předpokladů normality; specální případy; test linearity regrese a porovnání 2 regresních modelů; základy regresní diagnostiky. Korelace: korelační koeficient, koeficient mnohonásobné korelace a parciální korelační koeficient; jejich výběrové protějšky a testování.
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.