Course detail
Statistical Methods in Practice
FP-smpPAcad. year: 2019/2020
Introduction to modern methods of statistical processing of data and possibilities of using statistical software, for example, tasks associated with analysis of the survey.
Supervisor
Department
Learning outcomes of the course unit
Ability to work with specialized software, knowledge bases analysis surveys, methods of ANOVA and cluster analysis
Prerequisites
Elements of probability theory and mathematical statistics .
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
ŘEZANKOVÁ, Hana. Analýza dat z dotazníkových šetření : (druhé vydání). 2. vyd. Praha : Professional Publishing, 2010. 217 s. ISBN 978-807-4310-195. (CS)
BUDÍKOVÁ, Marie; KRÁLOVÁ, Maria; MAROŠ, Bohumil. Průvodce základními statistickými metodami. 1. vyd. Praha : Grada, 2010. 272 s. ISBN 978-802-4732-435. (CS)
ŘEZANKOVÁ, Hana; HÚSEK, Dušan; SNÁŠEL, Václav. Shluková analýza dat. 2., rozš. vyd. Praha : Professional Publishing, 2009. 218 s. ISBN 978-808-6946-818. (CS)
ŘEZANKOVÁ, Hana; HÚSEK, Dušan; SNÁŠEL, Václav. Shluková analýza dat. 2., rozš. vyd. Praha : Professional Publishing, 2009. 218 s. ISBN 978-808-6946-818. (CS)
ŘEZANKOVÁ, Hana. Analýza dat z dotazníkových šetření : (druhé vydání). 2. vyd. Praha : Professional Publishing, 2010. 217 s. ISBN 978-807-4310-195. (CS)
BUDÍKOVÁ, Marie; KRÁLOVÁ, Maria; MAROŠ, Bohumil. Průvodce základními statistickými metodami. 1. vyd. Praha : Grada, 2010. 272 s. ISBN 978-802-4732-435. (CS)
Planned learning activities and teaching methods
The lecture combines theory with illustrative examples. The practical exercise is concerned on handling numerical tasks.
Assesment methods and criteria linked to learning outcomes
credit
Condition of obtaining credit is Poro development seminar work as the tutor. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.
Language of instruction
Czech
Work placements
Not applicable.
Course curriculum
1) Processing of data sets, statistical hypothesis testing
2) Designing a questionnaire
3) Analysis of dependence
4) Compare Files
5) Principles of clustering
6) Biologically inspired algorithms
Aims
Students will be able to design appropriate questionnaire and then evaluate the data. They will be able to process how to use commonly available Microsoft Excel, so statisticly specialized software.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not compulsory but is recommended. Attendance at seminars is checked. Excused absence for student exercises can be replaced by a spare tasks.