Course syllabus RRASA - Applied Statistics in English (FRDIS - SS 2016/2017)


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Course title: Applied Statistics in English
Semester: SS 2016/2017
Course supervisor: Ing. Veronika Jadczaková, Ph.D.
Supervising department: Department of Applied Statistics and Demography (FRDIS)
Time allowance: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Prerequisites for registration: none
Type of study: usual, consulting
Form of teaching: lecture, seminar
Mode of completion and credits: Exam (6 credits)
 
Course objective:
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 content:
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

 
Teaching methods and workload (hours of workload):
Type of teaching methodDaily attendance
Direct teaching
     lecture28 h
     practice28 h
Self-study
     preparation for exam42 h
     preparation for regular assessment14 h
     preparation for regular testing28 h
     elaboration of reports28 h
Total168 h
 
Key words:
none
 
Course completion:
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.
 
Course methods: 12 hours of lectures and 28 hours of computer lab sessions, practicing mainly analyses in MS Excel environment. In total, 40 hours of instruction.
 
Reading list:
Basic:
Statistics for engineers and scientists. Boston: McGraw-Hill, 869 p. ISBN 0-07-121492-5.
GUJARATI, D N. Basic econometrics. 4th ed. Boston: McGraw Hill, 2003. 1002 p. ISBN 0-07-112342-3.
ADAMEC, V. Applied statistics: statistics I : descriptive statistics : linear regression and correlation : categorical data : time series : statistical indices. 1st ed. Brno: Mendelova univerzita v Brně, 2010. 119 p. ISBN 978-80-7375-455-6.

Study plans:
B-ITSE-IDSE International Development Studies, full-time form, initial academic year WS 2015/2016
B-RDE-SEDRE Socioeconomic and Environmental Development of Regions, full-time form, initial academic year WS 2015/2016
 
Run in the period of: WS 2019/2020, SS 2018/2019, WS 2018/2019, SS 2017/2018, WS 2017/2018, WS 2016/2017   (and older)
Course tutor: Ing. Veronika Jadczaková, Ph.D. (examiner, instructor, lecturer, supervisor)
Teaching language: English
Town: -- item not defined --


Last modification made by Bc. Vít Karber on 11/28/2016.

Type of output: