Course syllabus RRASA - Applied Statistics in English (FRDIS - WS 2019/2020)


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Course code:
RRASA
Course title in Czech:
Applied Statistics in English
Course title in English:
Applied Statistics in English
Semester: WS 2019/2020
Mode of completion and number of credits:
Exam (6 credits)
Mode of delivery and timetabled classes:
full-time, 2/2 (hours of lectures per week / hours of seminars per week)
Level of course:
bachelor
Course type:
optional
Type of delivery:
consulting
Mode of delivery for our mobility students abroad: -- item not defined --
Language of instruction:
English
Course supervisor:
Course supervising department:
Faculty:
Teachers:
Ing. Pavel Hrabec (examiner, instructor, lecturer)
doc. Ing. Kristina Somerlíková, Ph.D. (supervisor)
Prerequisites:
none
 
Timetable in this semester:
-- item not defined --
 
Aim of the course and learning outcomes:
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

Learning activities and teaching methods:
Type of teaching methodDaily attendance
lecture
28 h
practice
28 h
preparation for exam42 h
preparation for regular assessment
14 h
preparation for regular testing
28 h
elaboration of reports
28 h
Total
168 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.
 
Assessment criteria ratio:
Requirement type
Daily attendance
Total
0 %
 
Recomended reading and other learning resources:
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.

Course listed in study plans for this semester:
-- item not defined --
 
Course listed in previous semesters: SS 2019/2020, SS 2018/2019, WS 2018/2019, SS 2017/2018, WS 2017/2018, SS 2016/2017 (and older)
Teaching place:
-- item not defined --


Last modification made by Bc. Vít Karber on 11/20/2019.

Type of output: