M6130 Computational statistics

Faculty of Science
Spring 2025
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I || MUC51 Probability and Statistics
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

M6130 Computational statistics

Faculty of Science
Spring 2024
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 19. 2. to Sun 26. 5. Thu 12:00–13:50 M4,01024
  • Timetable of Seminar Groups:
M6130/01: Mon 19. 2. to Sun 26. 5. Mon 12:00–12:50 M6,01011, Mon 13:00–13:50 MP1,01014, M. Budíková
M6130/02: Mon 19. 2. to Sun 26. 5. Tue 10:00–11:50 M4,01024, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I || MUC51 Probability and Statistics
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2023
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Wed 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Mon 14:00–14:50 M2,01021, Mon 15:00–15:50 MP1,01014, M. Budíková
M6130/02: Wed 12:00–12:50 M6,01011, Wed 13:00–13:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I || MUC51 Probability and Statistics
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2022
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Mon 14:00–15:50 M2,01021
  • Timetable of Seminar Groups:
M6130/01: Tue 8:00–8:50 M1,01017, Tue 9:00–9:50 MP1,01014, M. Budíková
M6130/02: Mon 10:00–10:50 M6,01011, Mon 11:00–11:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I || MUC51 Probability and Statistics
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2021
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Mon 1. 3. to Fri 14. 5. Wed 14:00–15:50 online_M2
  • Timetable of Seminar Groups:
M6130/01: Mon 1. 3. to Fri 14. 5. Mon 10:00–11:50 online_MP1, M. Budíková
Prerequisites
M7521 Probability and Statistics || MUC51 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2020
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Mon 14:00–15:50 M2,01021
  • Timetable of Seminar Groups:
M6130/01: Wed 14:00–14:50 M5,01013, Wed 15:00–15:50 MP1,01014, Wed 15:00–15:50 MP2,01014a, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to teach students
perform exploratory analysis of one-dimensional and multidimensional data;
use parametric and nonparametric tests of one, two or more populations;
analyze data dependencies;
perform goodness–of-fit tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
  • Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
  • Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
  • Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
  • Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2019
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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 18. 2. to Fri 17. 5. Fri 10:00–11:50 M2,01021
  • Timetable of Seminar Groups:
M6130/01: Mon 18. 2. to Fri 17. 5. Fri 12:00–12:50 M2,01021, Fri 13:00–13:50 MP1,01014, M. Budíková
M6130/02: Mon 18. 2. to Fri 17. 5. Fri 8:00–8:50 M3,01023, Fri 9:00–9:50 MP2,01014a, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
At the end of this course, students - will have a good knowledge of STATISTICA system; - would be able to describe real data sets using tables, statistical graphs and numerical characteristics; - would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write one test. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
spring 2018
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Fri 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Wed 14:00–14:50 M4,01024, Wed 15:00–15:50 MP1,01014, M. Budíková
M6130/02: Tue 8:00–8:50 M6,01011, Tue 9:00–9:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
At the end of this course, students - will have a good knowledge of STATISTICA system; - would be able to describe real data sets using tables, statistical graphs and numerical characteristics; - would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Learning outcomes
At the end of this course, students
will have a good knowledge of STATISTICA system;
would be able to describe real data sets using tables, statistical graphs and numerical characteristics;
would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write one test. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2017
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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 20. 2. to Mon 22. 5. Fri 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Mon 20. 2. to Mon 22. 5. Fri 10:00–10:50 M1,01017, Fri 11:00–11:50 MP2,01014a, Fri 11:00–11:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
At the end of this course, students - will have a good knowledge of STATISTICA system; - would be able to describe real data sets using tables, statistical graphs and numerical characteristics; - would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2016
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Thu 14:00–15:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Mon 8:00–8:50 M6,01011, Mon 9:00–9:50 MP1,01014, M. Budíková
M6130/02: Thu 11:00–11:50 M4,01024, Thu 12:00–12:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
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 11 fields of study the course is directly associated with, display
Course objectives
At the end of this course, students - will have a good knowledge of STATISTICA system; - would be able to describe real data sets using tables, statistical graphs and numerical characteristics; - would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2015
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Petra Ráboňová, 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
Thu 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Thu 18:00–19:50 MP1,01014, Thu 18:00–19:50 M6,01011, P. Ráboňová
M6130/02: Thu 16:00–17:50 M6,01011, Thu 16:00–17:50 MP1,01014, P. Ráboňová
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
At the end of this course, students - will have a good knowledge of STATISTICA system; - would be able to describe real data sets using tables, statistical graphs and numerical characteristics; - would be able to testing statistical hypothesis using parametrics and nonparametrics tests.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
Assessment methods
During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2014
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Thu 8:00–9:50 M5,01013
  • Timetable of Seminar Groups:
M6130/01: Thu 10:00–10:50 M6,01011, Thu 11:00–11:50 MP1,01014, M. Budíková
M6130/02: Tue 10:00–10:50 M6,01011, Tue 11:00–11:50 MP1,01014, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2013
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Stanislav Abaffy (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
Wed 10:00–11:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Thu 11:00–11:50 M6,01011, Thu 12:00–12:50 MP1,01014, M. Budíková
M6130/02: Thu 9:00–9:50 M4,01024, Thu 10:00–10:50 MP1,01014, M. Budíková
M6130/03: Thu 16:00–16:50 M6,01011, Thu 17:00–17:50 MP1,01014, S. Abaffy
M6130/04: Thu 18:00–18:50 M6,01011, Thu 19:00–19:50 MP1,01014, S. Abaffy
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2012
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Petr Okrajek (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
Wed 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Mon 10:00–10:50 M4,01024, Mon 11:00–11:50 MP1,01014, M. Budíková
M6130/02: Thu 8:00–8:50 M3,01023, Thu 9:00–9:50 MP1,01014, M. Budíková
M6130/03: Mon 18:00–18:50 M6,01011, Mon 19:00–19:50 MP1,01014, P. Okrajek
M6130/04: Tue 18:00–18:50 M6,01011, Tue 19:00–19:50 MP1,01014, P. Okrajek
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
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
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Spring 2011
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Petr Okrajek (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Mon 10:00–10:50 M6,01011, Mon 11:00–11:50 MP1,01014, M. Budíková
M6130/02: Thu 8:00–8:50 M4,01024, Thu 9:00–9:50 MP1,01014, P. Okrajek
M6130/03: Thu 10:00–10:50 M4,01024, Thu 11:00–11:50 MP1,01014, P. Okrajek
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The discipline contains exploratory and regression data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis: box-plot, N-P plot, histogram, empirical distribution function, moments, multivariate data samples, graphical representation of dependence two or more variables, cluster analysis. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient. Regression analysis: classical linear regression model, least squares method, tests for regression parameters.
Literature
    required literature
  • BUDÍKOVÁ, Marie, Tomáš LERCH and Štěpán MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita, 2005, 170 pp. ISBN 978-80-210-3886-8. info
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
    recommended literature
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students solve a written test. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2010
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Petr Okrajek (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Thu 8:00–8:50 M4,01024, Thu 9:00–9:50 MP1,01014, M. Budíková
M6130/02: Thu 10:00–10:50 M4,01024, Thu 11:00–11:50 MP1,01014, M. Budíková
M6130/03: Fri 8:00–8:50 M3,01023, Fri 9:00–9:50 MP1,01014, M. Budíková
M6130/04: Thu 9:00–9:50 M4,01024, Thu 10:00–10:50 MP1,01014, P. Okrajek
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2009
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
M6130/01: Fri 8:00–9:50 M4,01024, Fri 8:00–9:50 MP2,01014a, Fri 8:00–9:50 MP1,01014, M. Budíková
M6130/02: Tue 10:00–11:50 M2,01021, Tue 10:00–11:50 MP1,01014, Tue 10:00–11:50 MP2,01014a, M. Budíková
M6130/03: Tue 8:00–9:50 MP1,01014, Tue 8:00–9:50 MP2,01014a, Tue 8:00–9:50 M2,01021, M. Budíková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2008
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Pavla Krajíčková, Ph.D. (seminar tutor)
Mgr. Tomáš Lerch (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 12:00–13:50 N21
  • Timetable of Seminar Groups:
M6130/01: Fri 14:00–14:50 N41, Fri 15:00–15:50 M3,04005 - dříve Janáčkovo nám. 2a, T. Lerch
M6130/02: Thu 16:00–16:50 N41, Thu 17:00–17:50 M3,04005 - dříve Janáčkovo nám. 2a, P. Krajíčková
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon, Van der Waerden and median tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2h přednášky, 2h cvičení. Všechna cvičení probíhají v počítačové učebně s využitím speciálního statistického software. Zkouška je písemná, je doplněna konkrétním zpracováním dat u počítače.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2007
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable
Tue 11:00–12:50 N21
  • Timetable of Seminar Groups:
M6130/01: Thu 12:00–12:50 N41, Thu 13:00–13:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Budíková
M6130/02: Mon 12:00–12:50 N21, Mon 13:00–13:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Budíková
M6130/03: Thu 13:00–13:50 N41, Thu 14:00–14:50 M3,04005 - dříve Janáčkovo nám. 2a, T. Lerch
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon, Van der Waerden and median tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2h přednášky, 2h cvičení. Všechna cvičení probíhají v počítačové učebně s využitím speciálního statistického software. Zkouška je písemná, je doplněna konkrétním zpracováním dat u počítače.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2006
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable
Thu 17:00–18:50 N41
  • Timetable of Seminar Groups:
M6130/01: Tue 8:00–8:50 N41, Tue 9:00–9:50 M3,04005 - dříve Janáčkovo nám. 2a
M6130/02: Mon 12:00–12:50 N21, Mon 13:00–13:50 M3,04005 - dříve Janáčkovo nám. 2a
M6130/03: Tue 12:00–12:50 N21, Tue 13:00–13:50 M3,04005 - dříve Janáčkovo nám. 2a
Prerequisites
M7521 Probability and Statistics || M3121 Probability
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon, Van der Waerden and median tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2h přednášky, 2h cvičení. Všechna cvičení probíhají v počítačové učebně s využitím speciálního statistického software. Zkouška je písemná, je doplněna konkrétním zpracováním dat u počítače.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2005
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer), RNDr. Štěpán Mikoláš (deputy)
RNDr. Štěpán Mikoláš (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable
Tue 12:00–13:50 N41
  • Timetable of Seminar Groups:
M6130/01: Thu 16:00–16:50 N41, Thu 17:00–17:50 M3,04005 - dříve Janáčkovo nám. 2a, Fri 11:00–11:50 N41, Fri 12:00–12:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Budíková, Rozvrhově doporučeno pro učitelské kombinace.
M6130/02: No timetable has been entered into IS. Š. Mikoláš, Rozvrhově doporučeno pro M a Me.
Prerequisites
M7521 Probability and Statistics || M3121 Probability
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon, Van der Waerden and median tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2h přednášky, 2h cvičení. Všechna cvičení probíhají v počítačové učebně s využitím speciálního statistického software. Zkouška je písemná, je doplněna konkrétním zpracováním dat u počítače.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2004
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer), RNDr. Štěpán Mikoláš (deputy)
RNDr. Štěpán Mikoláš (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable of Seminar Groups
M6130/01: No timetable has been entered into IS. Š. Mikoláš, Rozvrhově doporučeno 4;M,1,2,5,12,72
M6130/02: No timetable has been entered into IS. Š. Mikoláš, Rozvrhově doporučeno 3;Mb,Me
Prerequisites (in Czech)
M7521 Probability and 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
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2003
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
RNDr. Marie Budíková, Dr.
Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Timetable of Seminar Groups
M6130/01: No timetable has been entered into IS. M. Budíková
M6130/02: No timetable has been entered into IS. M. Budíková
Prerequisites (in Czech)
M7521 Probability and 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 is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Autumn 2024

The course is not taught in Autumn 2024

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Autumn 2023

The course is not taught in Autumn 2023

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Autumn 2022

The course is not taught in Autumn 2022

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
autumn 2021

The course is not taught in autumn 2021

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Autumn 2020

The course is not taught in Autumn 2020

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
Autumn 2019

The course is not taught in Autumn 2019

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and 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 11 fields of study the course is directly associated with, display
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Autumn 2008

The course is not taught in Autumn 2008

Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and Statistics I
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Autumn 2007

The course is not taught in Autumn 2007

Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M7521 Probability and Statistics || M3121 Probability and Statistics I
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Computational statistics

Faculty of Science
spring 2012 - acreditation

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

Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (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
Prerequisites
M7521 Probability and Statistics || M3121 Probability and Statistics I
M7521 or M3121
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Základní statistické metody.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2011 - only for the accreditation
Extent and Intensity
2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
M7521 Probability and Statistics I || M3121 Probability
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples. Upon completing this course, students will master the basic techniques of statistical software data analysis and will understand fundamental principles of selected statistical methods.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Teaching methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
Assessment methods
At the end of semester, students submit a written task. The examination is written and is complemented by practical computer aided data analysis.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M6130 Fundamental statistical methods

Faculty of Science
Spring 2008 - for the purpose of the accreditation
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
Guaranteed by
RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: RNDr. Marie Budíková, Dr.
Prerequisites
M7521 Probability and Statistics I || M3121 Probability
M7521 or M3121
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The discipline contains exploratory data analysis, nonparametric statistics, statistical tests employed with two samples and with three and more samples, goodness-of-fit tests and statistical tests employed with multivariate samples.
Syllabus
  • Exploratory data analysis:histogram, empirical distribution function, moments, description of time series, indexes, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon, Van der Waerden and median tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogenity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.
Literature
  • MICHÁLEK, Jaroslav. Biometrika. 1. vyd. Praha: Státní pedagogické nakladatelství, 1982, 404 s. info
  • ZVÁRA, Karel. Biostatistika. 1. vyd. Praha: Karolinum, 1998, 210 s. ISBN 8071847739. info
  • ANDĚL, Jiří. Statistické metody. 1. vydání. Praha: MATFYZPRESS, 1993, 246 s. info
  • CLEVELAND, William S. Visualizing data. Murray Hill: AT & T Bell Laboratories, 1993, 360 s. ISBN 0-9634884-0-6. info
Assessment methods (in Czech)
Výuka probíhá každý týden v rozsahu 2h přednášky, 2h cvičení. Všechna cvičení probíhají v počítačové učebně s využitím speciálního statistického software. Zkouška je písemná, je doplněna konkrétním zpracováním dat u počítače.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.math.muni.cz/~budikova
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)