Sylabus predmetu RRASA - Applied Statistics in English (FRDIS - SS 2013/2014)

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Course code: RRASA
Course title in language of instruction: Applied Statistics in English
Course title in Czech: Applied Statistics in English
Course title in English: Applied Statistics in English
Mode of completion and number of credits: Exam (6 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Language of instruction: English
Level of course: bachelor
Semester: SS 2013/2014
Name of lecturer: Ing. Veronika Jadczaková, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites: none
Aims of the course:
Acquirement of theoretical knowledge and basic calculation procedures in the field of descriptive statistics, assessment of relationships between numerical and categorical variables, description of time series and statistical comparisons as applied to social and economic processes. Skills to use statistical tools and functions of Microsoft Excell package.
Course contents:
1.Introduction into statistics (allowance 2/4)
a.The term of statistics, methods, terminology, stages of statistical investigation
b.Statistical means of expressing results, tables and diagrams
c.Univariate statistical series, distribution of frequencies, variable classification
d.Important values: extremes, mode, quantiles
e.Analysis of structure, Lorenz curve and Gini index

2.Descriptive statistics (allowance 4/10)
a.Measuring the location and variability
b.Moments and system of moment characteristics
c.Basic types of statistical dependencies, terminology
d.Regression and the least squares method, correlation index, determination
e.Simple linear correlation, joint regression lines and correlation coefficient
f.Classification of categorical data, analysis of contingency tables

3.Statistical dynamics (allowance 6/14)
a.Time series, definition, types, derived time series
b.Elementary characteristics of dynamic events; moving averages
c.Description of trend; trivial model of seasonality; assessment of model quality
d.Measurements and comparisons of magnitude of particular events, types of quantities for comparisons, definitions and types of relative numbers
e.Indices in wide and narrow sense, concept, types. Individual complex indices
f.Aggregate indices, value index and related indices, decomposition of indices and corresponding absolute differences

Learning outcomes and competences:
Generic competences:
-Ability to solve problems
-Ability to work independently
-Basic computing skills
-Science and research skills
-Skilled at utilizing and processing information

Specific competences:
-Ability of numeric and graphic presentation of mass data analysis
-Ability to analyse mass sectional and dynamic data
-Ability to interpret results and apply statistical knowledge
-Ability to manage basic principles of quantitative and qualitative mass data collection and processing

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -
Learning activities and study load (hours of study load):
Type of teaching methodDaily attendance
Direct teaching
     lecture28 h
     practice28 h
     preparation for exam42 h
     preparation for regular assessment14 h
     preparation for regular testing28 h
     elaboration of reports28 h
Total168 h
Assessment methods:
Hand in 4 assignments to be solved individually and solution of 2 partial written tests, each of 60-minute duration focused on practical examples and theory. The sum of scores from assignments and tests is credited for final grade with 50% pass rate.
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQStatistics for engineers and scientistsBostonMcGraw-Hill0-07-121492-5
RQADAMEC, V.Statistics IEdiční středisko, MZLU v Brně2004
RQGUJARATI, D N.Basic econometricsBostonMcGraw Hill20030-07-112342-3


Last modification made by Bc. Vít Karber on 02/03/2014.

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