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).
Taught in person.
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
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)
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.
  • Enrolment Statistics (Spring 2022, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2022/M6130