Z2069 Geographical Data Analysis 2

Faculty of Science
Spring 2019
Extent and Intensity
1/1/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
prof. RNDr. Petr Dobrovolný, CSc. (lecturer)
Mgr. Tomáš Čejka, Ph.D. (seminar tutor)
Mgr. Klára Čížková, Ph.D. (seminar tutor)
Ing. Pavla Matějů (seminar tutor)
Mgr. et Mgr. Pavel Ugwitz, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Rudolf Brázdil, DrSc.
Department of Geography – Earth Sciences Section – Faculty of Science
Contact Person: prof. RNDr. Petr Dobrovolný, CSc.
Supplier department: Department of Geography – Earth Sciences Section – Faculty of Science
Timetable
Mon 18. 2. to Fri 17. 5. Tue 14:00–14:50 A,01026
  • Timetable of Seminar Groups:
Z2069/01: Mon 18. 2. to Fri 17. 5. Wed 15:00–15:50 Z1,01001b, T. Čejka, K. Čížková, P. Matějů, P. Ugwitz
Z2069/02: Mon 18. 2. to Fri 17. 5. Wed 14:00–14:50 Z1,01001b, T. Čejka, K. Čížková, P. Matějů, P. Ugwitz
Z2069/03: Mon 18. 2. to Fri 17. 5. Wed 17:00–17:50 Z1,01001b, T. Čejka, K. Čížková, P. Matějů, P. Ugwitz
Z2069/04: Mon 18. 2. to Fri 17. 5. Wed 18:00–18:50 Z1,01001b, T. Čejka, K. Čížková, P. Matějů, P. Ugwitz
Prerequisites (in Czech)
KREDITY_MIN ( 19 )
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 80 student(s).
Current registration and enrolment status: enrolled: 0/80, only registered: 0/80
fields of study / plans the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
The main aim of this course is to provide students with an overview of practical use of descriptive statistic. At the end of the course student should be able to understand basic statistical methods explained in individual lectures. He/she would be able to explain when to apply different methods and make reasoned decisions about preconditions that are necessary for proper utilization of methods in question. He/she would be able to work with information on data preparation, make deductions based on acquired knowledge concerning statistical methods and properly interpret results
Syllabus
  • Analysis of variance I – basic terms, the One Factor ANOVA,
  • Analysis of variance II – the Two Factor ANOVA, multiple comparisons
  • Extending Correlation Analysis – nonlinear dependence, multiple and partial correlation
  • Extending Regression Analysis – model testing and verification, multiple regression
  • Nonparametric statistics – goodness of fit, nonparametric ANOVA
  • Time series analysis I. – model identification, autocorrelation
  • Time series analysis II. – AR & MA models, the frequency approach
  • Introductory multivariate statistics, Principle Component Analysis
  • Cluster analysis, classification algorithms
Literature
    recommended literature
  • HENDL, Jan. Přehled statistických metod zpracování dat :analýza a metaanalýza dat. Vyd. 1. Praha: Portál, 2004, 583 s. ISBN 8071788201. info
  • BURT, James E., Gerald M. BARBER and David L. RIGBY. Elementary statistics for geographers. 3rd ed. New York: Guilford Press, 2009, xii, 653. ISBN 9781572304840. info
  • ROGERSON, Peter. Statistical methods for geography : a student's guide. 3rd ed. Los Angeles: Sage, 2010, xvi, 348. ISBN 9781848600034. info
  • MAINDONALD, J. H. Data analysis and graphics using R : an example-based approach. Edited by John Braun. New York: Cambridge University Press, 2003, xxiii, 362. ISBN 0521813360. info
Teaching methods
Lectures explaining basic terms and presenting individual examples step by step. Practical training based on 10 exercises that are solved using statistical software.
Assessment methods
Written test at the end of the term. Elaboration of all practical excercises is the necessary conditon for credits. Student needs at least 60% of points form the theoretical test. Excelent practical excercises are evaluated by 10%. Students with excelent exercises need 50% of points from the theoretical test.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2019, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2019/Z2069