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.
  • Enrolment Statistics (Spring 2011, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2011/M6130