MA012 Statistics II

Faculty of Informatics
Autumn 2015
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Eva Janoušková, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Faculty of Informatics
Supplier department: Faculty of Science
Timetable
Mon 14:00–15:50 A318
  • Timetable of Seminar Groups:
MA012/T01: Wed 23. 9. to Tue 22. 12. Wed 14:40–16:15 106, E. Janoušková, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
MA012/01: Mon 16:00–17:50 B116, O. Pokora
MA012/02: Thu 12:00–13:50 A320, P. Ráboňová
MA012/03: Thu 14:00–15:50 A320, P. Ráboňová
MA012/04: Mon 18:00–19:50 B116, Tato seminární skupina je rezerva a může být otevřena jen v případě potřeby (výrazně překročená kapacita) a při schopnosti personálního zajištění výuky ze strany PřF. Studenti se musí přednostně hlásit do skupin 01--03.
Prerequisites
Prerequisites: calculus in one and several variables, basics of linear algebra, probability and statistics from course MV011 Statistics I.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 23 fields of study the course is directly associated with, display
Course objectives
Upon completing this course, students will be able: to apply advanced statistical method for real datasets; to understand the corresponding algorithms and calculations; to statistically analyze multivariate data; to employ the free statistical software R.
Syllabus
  • Random sample and its properties;
  • One-factor ANOVA;
  • Correlation analysis;
  • Nonparametric statistical tests;
  • Goodness-of-fit tests;
  • Test of independence;
  • Multivariate regression model;
  • Analysis of residuals;
  • Generalized linear models;
Literature
  • ANDĚL, J. Základy matematické statistiky. Praha: MFF UK, 2005. info
  • RAO, C. Radhakrishna. Lineární metody statistické indukce a jejich aplikace. Translated by Josef Machek. Vyd. 1. Praha: Academia, 1978, 666 s. URL info
  • BERNSTEIN, Stephen and Ruth BERNSTEIN. Schaum's outline of theory and problems of elements of statistics : descriptive statistics and probability. New York, N.Y.: McGraw-Hill, 1999, vii, 354. ISBN 0070050236. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written with short oral discussion on student's project. At least 50 % of the total points are required for successful completiton of the course.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2015, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2015/MA012