Applied Statistics

Studijní plán: Erasmus - Počítačové systémy - příjezd na krátkodobý studijní pobyt

PředmětApplied Statistics (ASTa-1)
GarantujeKatedra matematiky (KM)
GarantRNDr. Ing. Martina Zámková, Ph.D. ( jarosovm@vspj.cz )
JazykAnglicky
Počet kreditů7
Ekvivalent
Prezenční studium
Přednáška2 h
Cvičení4 h
Studijní plán Typ Sem. Kred. Ukon.
Cestovní ruch - platný pro studenty, kteří započali studium od ZS 2013/2014 P 3 7 kr. Z,ZK
Cestovní ruch - platný pro studenty, kteří započali studium od ZS 2016/2017 P 3 7 kr. Z,ZK
Erasmus - Cestovní ruch - příjezd na krátkodobý studijní pobyt P 1 7 kr. Z,ZK
Erasmus - Finance a řízení - příjezd na krátkodobý studijní pobyt P 1 7 kr. Z,ZK
Erasmus - Počítačové systémy - příjezd na krátkodobý studijní pobyt PV 1 7 kr. Z,KZ
Erasmus - Porodní asistentka - příjezd na krátkodobý studijní pobyt PV 1 7 kr. Z,KZ
Erasmus - Všeobecná sestra - příjezd na krátkodobý studijní pobyt PV 1 7 kr. Z,KZ
Erasmus - Zdravotně sociální pracovník - příjezd na krátkodobý studijní pobyt PV 1 7 kr. Z,KZ
Finance a řízení - platný pro studenty, kteří započali studium od ZS 2013/2014 P 3 7 kr. Z,ZK

Sylabus

  • 1. Descriptive statistics, basic statistical terms and its hierarchy, classification
    2. Measuring the level. Statistical computing packages
    3. Measuring the variability and further data set properties
    4. Bases of probability
    5. Random (stochastic) variables and its distributions, statistical tables
    6. Theory of statistical estimation, sampling methods
    7. Basic terms and dependence classification
    8. Least squares method, regression and correlation analysis
    9. Dependence of word characters (association and contingency)
    10. Economic time series, basic facts and simple characteristics
    11. Trend measurement, seasonality measurement. Total and complex indices, decomposition
    12. Time series further issues (adaptive methods, forecasting, dependence measurement)
    13. Testing statistical hypotheses - procedures in testing and possible errors
    14. Selected parametric and nonparametric tests

Doporučená literatura

  • Adamec, I. Applied statistics – Statistics. Brno: Mendelova Univerzita, 2010. ISBN 978-80-7375-455-6
  • Wonnacott, T., H., Wonnacott, R., J. Introductory statistics for business and economics. Ontario: John Wiley and sons, 1990. ISBN 978-0-471-61517-0

  • • NAVIDI, W. Statistics for engineers and scientists. Boston: McGraw-Hill, 2006. 869 s. McGraw-Hill international edition. ISBN 0-07-121492-5.
  • • Minařík, B. Statistika. Elektronická učebnice ve formátu PDF na CD ROM. Brno: Mendelova univerzita, 2013, 265 stran. ISBN 978-807375-721-2
    • Marek, L. a kol. Statistika v příkladech. Praha: Professional Publishing, 2013, 403 stran. ISBN 978-80-7431-118-5
    • HINDLS, R. a kol. Statistika pro ekonomy. 8. vyd. Praha: Professional Publishing, 2007. 415 s. ISBN 978-80-86946-43-6.
    • HRONOVÁ, S.,HINDLS, R., SEGER, J. Statistika pro ekonomy. Praha: Professional Publishing, 2006. 415 s. ISBN 80-86946-16-9.
    • NAVIDI, W. Statistics for engineers and scientists. Boston: McGraw-Hill, 2006. 869 s. McGraw-Hill international edition. ISBN 0-07-121492-5.
    • ARLT, J. ARLTOVÁ, M. Ekonomické časové řady : [vlastnosti, metody modelování, příklady a aplikace]. 1. vyd. Praha: Grada, 2007. 285 s. ISBN 978-80-247-1319-9.

Anotace

The aim of this course is practically useful statistical methods of economic events analysis.  Basic statistical concepts and procedures for processing and analysis of empirical data. The measurement dependencies of economic variables using regression and correlation analysis and measurement dependencies of categorical variables using contingency tables. Elements of the description and analysis of dynamic events – time series, their classification, measurement of level of dynamic events and elementary description o their development – simple models of trend and seasonality, adaptive methods, forecasting, dependence measurement. Furthermore, in terms of understanding the basic concepts of probability, estimation theory, the use of sampling methods and principles of work with random quantities of basic distribution, statistical hypothesis testing.


Knowledge: Student knows and is able to use basic principles of descriptive statistics – data classification and calculation of important values. Student knows basic statistical attributes of the dataset and is able to use principles of their evaluation using of statistical characteristics (level, variability and other properties, with emphasis on the characteristics based on moments). Student knows, how to use basic principles of statistical dependence evaluation of numeric and categorical variables and corresponding basic characteristics (regression function, index and coefficient of correlation, contingency and association coefficients). Student knows and is able to use basic principles of social - economic events dynamics evaluation (chronological mean, absolute and relative growth characteristics, moving averages, trend function, seasonal indices and constants, residual component characteristics). Student can work with probability distributions of selected distribution of discrete and continuous random variables. Student understands and knows the basic principles of statistical induction (point and interval estimation, hypothesis testing) based on random variables. Student is able to independent interpret of the relevant characteristics.


Skills: Student is able to evaluate simple or interval data classification and to present the results in the form of the chart or table and to evaluate important dataset values. Student is able to calculate statistical characteristics of the dataset from classified or unclassified data and to explain the results. Student is able to solve real regression or correlation tasks and to analyse contingency or association table. Student is able to describe real time series issues including aspects of level, trivial dynamics characteristics, trend and seasonality. Student is able to calculate basic individual and total indices and to explain the results. Student can solve problems with basic probability distributions. Student is able to construct a point and interval estimation of unknown parameters. Student can solve and interpret the results of basic tests of hypotheses. Students can work and solve problems in statistical computing packages, and interpret the results.


General qualification: Student has overview of the quantitative aspects of social - economic events.  Student is able to apply the knowledge in other courses of his field of study and by the bachelor work elaboration.

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