MA012 Statistics II

Faculty of Informatics
Autumn 2024
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
In-person direct teaching
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 26. 9. to Thu 19. 12. Thu 16:00–17:50 A217
  • Timetable of Seminar Groups:
MA012/01: Thu 26. 9. to Thu 19. 12. Thu 18:00–19:50 A215, O. Pokora
MA012/02: Wed 25. 9. to Wed 18. 12. Wed 8:00–9:50 A320, O. Pokora
MA012/03: Wed 25. 9. to Wed 18. 12. Wed 10:00–11:50 A320, O. Pokora
Prerequisites
Basic knowledge of calculus: function, derivative, definite integral.
Basic knowledge of linear algebra: matrix, determinant, eigenavlues, eigenvectors.
Knowledge of probability a and statistics and practice with statistical language R within the scope of course MB153 Statistics I or MB143 Design and analysis of statistical experiments. Students without these knowledges and without practice with R are adviced to complete the course MB153 first.
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
This is an advanced course which introduces students to more complex methods of mathematical statistics. It expands the knowledge from a basic course of statistics and add further methods. The lectures explains the mathematical background, algorithms, computational procedures and conditions, seminars lead to practical use of the methods for the analysis of datasets in statistical software R and to interprete the results. After completing the course, the student will understand advanced statistical methods and inferential principles (estimations, hypothesis testing). The student will be able to use this methods in analyzing datasets and will be able to statistically interpret the achieved results.
Learning outcomes
After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of a real dataset using tidyverse packages in software R;
- interpret the results obtained by the statistical analysis.
Syllabus
  • Analysis of variance (ANOVA).
  • Nonparametric tests – rank tests.
  • Goodness-of-fit tests.
  • Correlation analysis, correlation coefficients.
  • Multiple regression.
  • Regression diagnostics.
  • Autocorrelation and multicollinearity.
  • Principal component Analysis (PCA).
  • Logistic regression and other generalized linear models (GLM).
  • Contingency tables and independence testing.
  • Bootstrapping.
Literature
  • Navarro D. Learning Statistics with R. https://learningstatisticswithr.com/
  • SCHUMACKER, Randall E. Learning statistics using R. Los Angeles: Sage, 2015, xxiii, 623. ISBN 9781452286297. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
  • DAVIES, Tilman M. The book of R : a first course in programming and statistics. San Francisco: No Starch Press, 2016, xxxi, 792. ISBN 9781593276515. info
Teaching methods
Lectures and practical classes with computers (using R language with tidyverse environment).
Assessment methods
Evaluation is based on: 1) ROPOTS and problem solving suring practical classes – weight = 40 %, 2) final written exam – weight = 60 %. At least 50 % of averall points is needed to pass.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/fi/podzim2024/MA012/index.qwarp
Detailed information, schedule of lectures and practical classes and study materials for the current period are posted in the Interactive syllabus in IS.
The course is also listed under the following terms 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2023
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
RNDr. Radim Navrátil, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 8:00–9:50 A318
  • Timetable of Seminar Groups:
MA012/01: Wed 18:00–19:50 B011, O. Pokora
MA012/02: Wed 16:00–17:50 B011, O. Pokora
MA012/03: Tue 16:00–17:50 A215, R. Navrátil
Prerequisites
Basic knowledge of calculus: function, derivative, definite integral.
Basic knowledge of linear algebra: matrix, determinant, eigenavlues, eigenvectors.
Knowledge of probability a and statistics and practice with statistical language R within the scope of course MB153 Statistics I or MB143 Design and analysis of statistical experiments. Students without these knowledges and without practice with R are adviced to complete the course MB153 first.
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 25 fields of study the course is directly associated with, display
Course objectives
This is an advanced course which introduces students to more complex methods of mathematical statistics. It expands the knowledge from a basic course of statistics and add further methods. The lectures explains the mathematical background, algorithms, computational procedures and conditions, seminars lead to practical use of the methods for the analysis of datasets in statistical software R and to interprete the results. After completing the course, the student will understand advanced statistical methods and inferential principles (estimations, hypothesis testing). The student will be able to use this methods in analyzing datasets and will be able to statistically interpret the achieved results.
Learning outcomes
After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of a real dataset using tidyverse packages in software R;
- interpret the results obtained by the statistical analysis.
Syllabus
  • Analysis of variance (ANOVA).
  • Nonparametric tests – rank tests.
  • Goodness-of-fit tests.
  • Correlation analysis, correlation coefficients.
  • Multiple regression.
  • Regression diagnostics.
  • Autocorrelation and multicollinearity.
  • Principal component Analysis (PCA).
  • Logistic regression and other generalized linear models (GLM).
  • Contingency tables and independence testing.
  • Bootstrapping.
Literature
  • Navarro D. Learning Statistics with R. https://learningstatisticswithr.com/
  • SCHUMACKER, Randall E. Learning statistics using R. Los Angeles: Sage, 2015, xxiii, 623. ISBN 9781452286297. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
  • DAVIES, Tilman M. The book of R : a first course in programming and statistics. San Francisco: No Starch Press, 2016, xxxi, 792. ISBN 9781593276515. info
Teaching methods
Classes are in full-time form: 2 hours of lectures, 2 hours of practical classes a week.
Practical classes consist of work in statistical software R using tidyverse packages.
Assessment methods
Exercises: attendance and active involvement in problem solving and homeworks, working with ROPOTs, solving interim and final problems. Final examination: full-time form – written exam. ROPOTs, final problem solving and the exam are evaluated in points. For successful completion of the course, to achieve at least 50 % of total sum of points is necessary.
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/fi/podzim2023/MA012/index.qwarp
Detailed information, schedule of lectures and practical classes and study materials for the current period are posted in the Interactive syllabus in IS.
The course is also listed under the following terms 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 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2022
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Markéta Zoubková (seminar tutor)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 16:00–17:50 A217
  • Timetable of Seminar Groups:
MA012/01: Mon 18:00–19:50 A215, O. Pokora
MA012/02: Thu 16:00–17:50 A215, M. Zoubková
MA012/03: Thu 14:00–15:50 A215, M. Zoubková
Prerequisites
Basic knowledge of calculus: function, derivative, definite integral.
Basic knowledge of linear algebra: matrix, determinant, eigenavlues, eigenvectors.
Knowledge of probability a and statistics and practice with statistical language R within the scope of course MB153 Statistics I or MB143 Design and analysis of statistical experiments. Students without these knowledges and without practice with R are adviced to complete the course MB153 first.
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 25 fields of study the course is directly associated with, display
Course objectives
The course introduces students to advanced methods of mathematical statistics – explains the algorithms, computational procedures, conditions, interpretation of results and practical use of these methods for the analysis of datasets in statistical software R. After completing the course, the student will understand advanced statistical methods and inferential principles (estimations, hypothesis testing). The student will be able to use this methods in analyzing datasets and will be able to statistically interpret the achieved results.
Learning outcomes
After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of a real dataset using tidyverse packages in software R;
- interpret the results obtained by the statistical analysis.
Syllabus
  • Analysis of variance (ANOVA).
  • Nonparametric tests – rank tests.
  • Goodness-of-fit tests.
  • Correlation analysis, correlation coefficients.
  • Multiple regression.
  • Regression diagnostics.
  • Autocorrelation and multicollinearity.
  • Principal component Analysis (PCA).
  • Logistic regression and other generalized linear models (GLM).
  • Contingency tables and independence testing.
  • Bootstrapping.
Literature
  • Navarro D. Learning Statistics with R. https://learningstatisticswithr.com/
  • SCHUMACKER, Randall E. Learning statistics using R. Los Angeles: Sage, 2015, xxiii, 623. ISBN 9781452286297. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
  • DAVIES, Tilman M. The book of R : a first course in programming and statistics. San Francisco: No Starch Press, 2016, xxxi, 792. ISBN 9781593276515. info
Teaching methods
Classes are in full-time form: 2 hours of lectures, 2 hours of practical classes a week.
Practical classes consist of work in statistical software R using tidyverse packages and of discussions.
Assessment methods
Exercises: attendance and active involvement in problem solving and homeworks, working with ROPOTs, solving interim and final problems. Final examination: full-time form – written exam. ROPOTs, final problem solving and the exam are evaluated in points. For successful completion of the course, to achieve at least 50 % of total sum of points is necessary.
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/fi/podzim2022/MA012/index.qwarp
Detailed information, schedule of lectures and practical classes and study materials for the current period are posted in the Interactive syllabus in IS.
The course is also listed under the following terms 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 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2021
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Markéta Zoubková (seminar tutor)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 15. 9. to Wed 15. 12. Wed 16:00–17:50 A318
  • Timetable of Seminar Groups:
MA012/01: Wed 15. 9. to Wed 8. 12. Wed 18:00–19:50 A215, O. Pokora
MA012/02: Thu 16. 9. to Thu 9. 12. Thu 14:00–15:50 A215, M. Zoubková
MA012/03: Thu 16. 9. to Thu 9. 12. Thu 16:00–17:50 A215, M. Zoubková
Prerequisites
Prerequisites: calculus, basics of linear algebra, probability and statistics (including basic experience with software R) from course MV011 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 25 fields of study the course is directly associated with, display
Course objectives
The course introduces students to advanced methods of mathematical statistics -- explains the algorithms, computational procedures, conditions, interpretation of results and practical use of these methods for the analysis of real datasets in statistical software R. After completing the course, the student will understand the principles of advanced statistical methods (analysis of variance, nonparametric tests, goodness-of-fit tests, correlation analysis, principal component analysis, generalized linear models, regression diagnostics, independence testing), will be able to use them in analyzing real datasets and will be able to interpret the results.
Learning outcomes
After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of the real dataset in the software R;
- interpret the results obtained by the statistical analysis.
Syllabus
  • Analysis of variance (ANOVA): one- and two-factor, with interactions.
  • Nonparametric tests: rank tests.
  • Goodness-of-fit tests.
  • Correlation analysis, correlation coefficients, rank correlation coefficients.
  • Regression diagnostics.
  • Autocorrelation, multicollinearity.
  • Principal component Analysis (PCA).
  • Generalized linear models (GLM): logistic regression and use of ROC curve, some other GLM.
  • Contingency tables and independence testing.
Literature
  • Navarro D. Learning Statistics with R. https://learningstatisticswithr.com/
  • SCHUMACKER, Randall E. Learning statistics using R. Los Angeles: Sage, 2015, xxiii, 623. ISBN 9781452286297. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
  • DAVIES, Tilman M. The book of R : a first course in programming and statistics. San Francisco: No Starch Press, 2016, xxxi, 792. ISBN 9781593276515. info
Teaching methods
Classes are in full-time form: lectures = 2 hours a week, practical classes = 2 hours a week – in R software, discussions. In the case of a regulation of distance learning, lectures and practical classes will continue online in MS Teams.
Assessment methods
Exercises: attendance and active involvement in problem solving and homeworks, working with ROPOTs, in-time solution of interim and final tasks. Final examination: full-time form – written exam. ROPOTs, final problem solving and the exam are evaluated in points, total achievable points >= 100. For successful completion, it is necessary to achieve at least 50 points. In the case of a regulation of distance learning: online work with a ROPOT – theoretical questions and problem solving.
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/fi/podzim2021/MA012/index.qwarp
Detailed information, schedule of lectures and practical classes and study materials for the current period are posted in the Interactive syllabus in IS.
The course is also listed under the following terms 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 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2020
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 16:00–17:50 A318
  • Timetable of Seminar Groups:
MA012/01: Mon 18:00–19:50 A215, O. Pokora
MA012/02: Thu 10:00–11:50 A215, O. Pokora
MA012/03: Thu 12:00–13:50 A215, O. Pokora
Prerequisites
Prerequisites: calculus, basics of linear algebra, probability and statistics (including basic experience with software R) from course MV011 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 25 fields of study the course is directly associated with, display
Course objectives
The course introduces students to advanced methods of mathematical statistics -- explains the algorithms, computational procedures, conditions, interpretation of results and practical use of these methods for the analysis of real datasets in statistical software R. After completing the course, the student will understand the principles of advanced statistical methods (analysis of variance, nonparametric tests, goodness-of-fit tests, correlation analysis, principal component analysis, generalized linear models, regression diagnostics, independence testing), will be able to use them in analyzing real datasets and will be able to interpret the results.
Learning outcomes
After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of the real dataset in the software R;
- interpret the results obtained by the statistical analysis.
Syllabus
  • Analysis of variance (ANOVA): one- and two-factor, with interactions.
  • Nonparametric tests: rank tests.
  • Goodness-of-fit tests.
  • Correlation analysis, correlation coefficients, rank correlation coefficients.
  • Regression diagnostics.
  • Autocorrelation, multicollinearity.
  • Principal component Analysis (PCA).
  • Generalized linear models (GLM): logistic regression and use of ROC curve, some other GLM.
  • Contingency tables and independence testing.
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures: 2 hours a week. Practical classes: 2 hour a week – in R software. Distance form: online lectures, practical classes and discussions.
Assessment methods
Exercises: active involvement in problem solving and homeworks, working with ROPOTs, in-time solution of interim and final tasks. Final examination: distance form. Distance form of the final exam: online work with a ROPOT, theoretical questions and problem solving. ROPOTs, final problem solving and the exam are evaluated in points, total achievable points >= 100. For successful completion, it is necessary to achieve at least 50 points.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/fi/podzim2020/MA012/index.qwarp
The course is also listed under the following terms 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 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2019
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. et Mgr. Daniela Kuruczová, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Thu 16:00–17:50 A217
  • Timetable of Seminar Groups:
MA012/T01: Wed 18. 9. to Sun 22. 12. Wed 10:00–11:50 A420, Thu 19. 9. to Sun 22. 12. Thu 9:00–10:40 115, D. Kuruczová, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MA012/01: Mon 8:00–9:50 A215, O. Pokora
MA012/02: Wed 14:00–15:50 A215, D. Kuruczová
MA012/03: Wed 16:00–17:50 A215, D. Kuruczová
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 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 25 fields of study the course is directly associated with, display
Course objectives
Course will introduce advanced statistical methods and usage of freely available software tool R.
Learning outcomes
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • One- and two-factor analysis of variance (ANOVA);
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Multivariate linear regression;
  • Correlation analysis, coefficients of correlation;
  • Autocorrelation, multicollinearity;
  • Generalized linear models (GLM);
  • Principal component analysis (PCA);
  • ROC curves, decision-making;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2018
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. et Mgr. Daniela Kuruczová, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Thu 12:00–13:50 A318
  • Timetable of Seminar Groups:
MA012/01: Mon 17. 9. to Mon 10. 12. Mon 8:00–9:50 B311, D. Kuruczová
MA012/02: Mon 17. 9. to Mon 10. 12. Mon 10:00–11:50 B311, D. Kuruczová
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 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 23 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • One- and two-factor analysis of variance (ANOVA);
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Multivariate linear regression;
  • Correlation analysis, coefficients of correlation;
  • Autocorrelation, multicollinearity;
  • Generalized linear models (GLM);
  • Principal component analysis (PCA);
  • ROC curves, decision-making;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2017
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. et Mgr. Daniela Kuruczová, Ph.D. (seminar tutor)
RNDr. Bc. Iveta Selingerová, Ph.D. (assistant)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Wed 8:00–9:50 A217
  • Timetable of Seminar Groups:
MA012/01: Wed 10:00–11:50 A215, O. Pokora
MA012/02: Thu 8:00–9:50 B117, D. Kuruczová
MA012/03: Thu 12:00–13:50 A215, D. Kuruczová
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 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 23 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • One- and two-factor analysis of variance (ANOVA);
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Multivariate linear regression;
  • Correlation analysis, coefficients of correlation;
  • Autocorrelation, multicollinearity;
  • Generalized linear models (GLM);
  • Principal component analysis (PCA);
  • ROC curves, decision-making;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2016
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. et Mgr. Daniela Kuruczová, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 10:00–11:50 A217
  • Timetable of Seminar Groups:
MA012/T01: Mon 19. 9. to Thu 22. 12. Mon 13:00–14:35 117, Thu 22. 9. to Thu 22. 12. Thu 12:20–13:55 117, E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MA012/01: Mon 14:00–15:50 B116, O. Pokora
MA012/02: Mon 16:00–17:50 B116, O. Pokora
MA012/03: Thu 8:00–9:50 A320, D. Kuruczová
MA012/04: Thu 10:00–11:50 A320, D. Kuruczová
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 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 23 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • One- and two-factor analysis of variance (ANOVA);
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Multivariate linear regression;
  • Correlation analysis, coefficients of correlation;
  • Autocorrelation, multicollinearity;
  • Generalized linear models (GLM);
  • Principal component analysis (PCA);
  • ROC curves, decision-making;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2015
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 14:00–15:50 A318
  • Timetable of Seminar Groups:
MA012/T01: Wed 23. 9. to Tue 22. 12. Wed 14:40–16:15 106, E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MA012/01: Mon 16:00–17:50 B116, O. Pokora
MA012/02: Thu 12:00–13:50 A320, P. Ráboňová
MA012/03: Thu 14:00–15:50 A320, P. Ráboňová
MA012/04: Mon 18:00–19:50 B116, Tato seminární skupina je rezerva a může být otevřena jen v případě potřeby (výrazně překročená kapacita) a při schopnosti personálního zajištění výuky ze strany PřF. Studenti se musí přednostně hlásit do skupin 01--03.
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 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 23 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • Random sample and its properties;
  • One-factor ANOVA;
  • Correlation analysis;
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Test of independence;
  • Multivariate regression model;
  • Analysis of residuals;
  • Generalized linear models;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2014
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Thu 12:00–13:50 A217
  • Timetable of Seminar Groups:
MA012/T01: Mon 15. 9. to Fri 19. 12. Mon 13:00–14:35 Učebna S4 (35a), Thu 18. 9. to Fri 19. 12. Thu 9:40–11:15 Učebna S6 (20), E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MA012/01: Wed 8:00–9:50 B204, P. Ráboňová
MA012/02: Wed 10:00–11:50 B204, P. Ráboňová
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 22 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
    required literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
    not specified
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. A necessary condition for the successful completion of the course is pass a test on the computer. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms 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 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2013
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 10:00–11:50 G101
  • Timetable of Seminar Groups:
MA012/01: Thu 18:00–19:50 G124, P. Ráboňová
MA012/02: Fri 14:00–15:50 G124, O. Černý
MA012/03: Wed 18:00–19:50 G125, O. Černý
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 22 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2012
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Ondřej Černý (seminar tutor)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
RNDr. Jan Vondra, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 10:00–11:50 G101
  • Timetable of Seminar Groups:
MA012/T01A: Thu 20. 9. to Fri 21. 12. Thu 16:00–17:55 Učebna S3 (37), E. Janoušková
MA012/T01AA: Wed 19. 9. to Fri 21. 12. Wed 10:00–11:55 Učebna S10 (56), E. Janoušková
MA012/01: Mon 12:00–13:50 G125, M. Řezáč
MA012/02: Tue 8:00–9:50 G125, O. Černý
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 23 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

MA012 Statistics II

Faculty of Informatics
Autumn 2011
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Kateřina Opršalová (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Timetable
Wed 14:00–15:50 A107
  • Timetable of Seminar Groups:
MA012/01: Thu 16:00–17:50 G123, K. Opršalová
MA012/02: Thu 18:00–19:50 G123, K. Opršalová
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 23 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2010
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics
Timetable
Tue 10:00–11:50 A107
  • Timetable of Seminar Groups:
MA012/01: Thu 10:00–11:50 B003, M. Řezáč
MA012/02: Thu 14:00–15:50 B003, M. Řezáč
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 22 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2009
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Martin Řezáč, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 10:00–11:50 A107
  • Timetable of Seminar Groups:
MA012/01: Thu 14:00–15:50 B003, M. Řezáč
MA012/02: Thu 16:00–17:50 B003, M. Řezáč
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 22 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2008
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Jana Meluzínová (seminar tutor)
RNDr. Tomáš Pavlík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 8:00–9:50 A107
  • Timetable of Seminar Groups:
MA012/01: Tue 10:00–11:50 B003, T. Pavlík
MA012/02: Wed 8:00–9:50 B011, J. Meluzínová
Prerequisites
Statistics II assume knowledges of fundamental statistical concepts in range 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 18 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2007
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
RNDr. Ivo Moll, CSc. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
prof. RNDr. Luboš Brim, CSc. (assistant)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 12:00–13:50 B204
  • Timetable of Seminar Groups:
MA012/01: Mon 8:00–9:50 B116, Mon 8:00–9:50 B007, T. Lerch
MA012/02: Mon 10:00–11:50 B116, Mon 10:00–11:50 B007, T. Lerch
Prerequisites (in Czech)
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika 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 18 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
  • http://home.zcu.cz/~friesl/hpsb/
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2006
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Ivo Moll, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
doc. RNDr. Lenka Přibylová, Ph.D. (seminar tutor)
prof. RNDr. Luboš Brim, CSc. (assistant)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 8:00–9:50 B003
  • Timetable of Seminar Groups:
MA012/01: Fri 8:00–9:50 B003, L. Přibylová
MA012/02: Fri 10:00–11:50 B003, L. Přibylová
Prerequisites (in Czech)
! M012 Statistics II
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika 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 6 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
  • http://home.zcu.cz/~friesl/hpsb/
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2005
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Ivo Moll, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
RNDr. Štěpán Mikoláš (seminar tutor)
prof. RNDr. Luboš Brim, CSc. (assistant)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 8:00–9:50 B003
  • Timetable of Seminar Groups:
MA012/01: Tue 10:00–11:50 B003, J. Koláček
MA012/02: Tue 12:00–13:50 B003, J. Koláček
Prerequisites (in Czech)
! M012 Statistics II
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika 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 6 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2004
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
RNDr. Ivo Moll, CSc. (lecturer)
RNDr. Štěpán Mikoláš (seminar tutor)
prof. RNDr. Luboš Brim, CSc. (assistant)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Timetable
Tue 9:00–10:50 B011
  • Timetable of Seminar Groups:
MA012/01: Tue 11:00–12:50 B011, I. Moll
MA012/02: Fri 10:00–11:50 B007, Š. Mikoláš
Prerequisites (in Czech)
! M012 Statistics II
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika I.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2003
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
prof. RNDr. Ladislav Skula, DrSc. (lecturer)
Mgr. Lucie Hampelová, Ph.D. (seminar tutor)
Mgr. Ing. Lukáš Rychnovský, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc.
Timetable
Tue 8:00–9:50 B003
  • Timetable of Seminar Groups:
MA012/01: Tue 10:00–11:50 B003, L. Hampelová
MA012/02: Fri 8:00–9:50 B007, L. Rychnovský
Prerequisites (in Czech)
! M012 Statistics II
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika I.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2002, 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.

MA012 Statistics II

Faculty of Informatics
Autumn 2002
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
prof. RNDr. Ladislav Skula, DrSc. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc.
Timetable
Mon 14:00–15:50 B204
  • Timetable of Seminar Groups:
MA012/01: Wed 12:00–13:50 B007
MA012/02: Wed 18:00–19:50 B007
Prerequisites (in Czech)
! M012 Statistics II
Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika I.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.
Syllabus
  • Basic ideas of inferential statistics. Samples and sample characteristics.
  • Properties of the point estimators.
  • Properties of the normal and asymptotically normal samples.
  • Interval estimators.
  • Statistical hypotheses testing.
  • Analysis of correlation.
  • Multidimensional linear regression.
  • Statistical computation pacquets.
Literature
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
  • OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms 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.
  • Enrolment Statistics (recent)