INMNKSTZ Statistical Data Processing

School of Business Administration in Karvina
Winter 2018
Extent and Intensity
0/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
prof. RNDr. Jaroslav Ramík, CSc. (lecturer)
Guaranteed by
prof. RNDr. Jaroslav Ramík, CSc.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Following the subject Statistics at bachelor's level to provide further explanation of mathematical statistics, the main findings of this theory and basic statistical methods. Present subject with regard to the application of the economic environment. Get computer skills and learn how to deal with the statistical data using SPSS and Excel on a computer.
Syllabus
  • 1. Analysis of variance (ANOVA) - One factor
    2. Analysis of variance - Two and more factors, nonparametric ANOVA
    3. Regression Analysis - One-dimensional, linear
    4. Regression Analysis - One-dimensional, nonlinear
    5. Regression Analysis - Multi-dimensional
    6. Regression Analysis - Multidimensional: Multicolinearity, heteroscedasticity, autocorrelation
    7. Basics of Time Series Analysis
    8. Trend analysis of time series
    9. Analysis of seasonal and random components of time series
    10. Exponential smoothing models of time series
    11. ARIMA models
    12. SARIMA models
    13. Forecasting of time series
    1. Analysis of variance (ANOVA) - One factor
    Independent and dependent factors, assumptions of analysis of variance with one factor. Procedure for the analysis of variance with one factor. The rate of dependence, determinative and correlation ratio.
    2. Analysis of variance - Two and more factors, nonparametric ANOVA
    Analysis of variance with two factors. Assumptions of ANOVA with two factors. Two-factor ANOVA without interaction and interaction. Kruskal-Wallis nonparametric ANOVA.
    3. Regression Analysis - One-dimensional, linear
    What is regression analysis (RA) - a simple, multiple, linear, nonlinear. The essence of simple linear RA - scatter diagram, regression, regression coefficients, adhesion, coefficient of determination, tests of hypotheses.
    4. Regression Analysis - One-dimensional, nonlinear
    Simple linear RA - basic types of nonlinearities, Törnquist curves and their applications in economics.
    5. Regression Analysis - Multi-dimensional
    Multiple linear RA - criterion, predictors, regression hyperplane, the coefficient of determination. Using the VRA for nominal predictors and correlation coefficients. Application examples of the economic area (marketing research).
    6. Regression Analysis - Multidimensional: Multicolinearity, heteroscedasticity, autocorrelation
    Population and sample regression function. Classical multivariate linear regression model. Multicollinearity and its causes. Heteroscedasticity, tests H-S (Park test, Bartley test) and its removal. Autocorrelation (sign test).
    7. Basics of Time Series Analysis
    Types of economic time series. Elemental characteristics of time series. Models of economic time series - decomposition, exponential smoothing, ARIMA.
    8. Trend analysis of time series
    Analytical methods for the determination of trends of time series: regression analysis (least squares method, maximum likelihood method). Synthetic methods: moving averages, exponential smoothing.
    9. Analysis of seasonal and random components of time series
    Analysis of seasonal component: models with constant seasonality with a step trend, with a linear trend. Models of proportional seasonality. Analysis of random component: statistical tests of random component using residues.
    10. Exponential smoothing models of time series
    Exponential smoothing models (simple, Holt, Winters model).
    11. ARIMA models
    Stochastic process and its stationarity. Fundamentals of ARIMA models: models AR, MA, I, ARIMA. Identification of ARIMA model using the autocorrelation function (ACF) and partial autocorrelation function (PACF). Calculation of ARIMA model, model verification, prediction of the ARIMA model.
    12. SARIMA models
    Identification of the SARIMA model using the autocorrelation function and partial autocorrelation function.
    13. Forecasting of time series
    The forecast ex-post and ex-ante, point and interval forecasts. Forecasting in linear regression models. Forecasting in ARIMA and SARIMA models.
Literature
    recommended literature
  • RAMÍK, J. a Š. ČEMERKOVÁ. Statistika A. Karviná: SU OPF, 2000. ISBN 80-85879-43-3. info
  • RAMÍK, R. a Š. ČEMERKOVÁ. Statistika B. Karviná: SU OPF, 2000. ISBN 80-7248-001-4. info
  • CYHELSKÝ, L., J. KAHOUNOVÁ a R. HINDLS. Elementární statistická analýza. Praha: Management Press, 1996. ISBN 80-7261-003-1. info
  • SEGER, J. a R. HINDLS. Statistické metody v tržním hospodářství. Praha: Victoria Publishing, 1995. ISBN 80-7187-058-7. info
  • GURAJATI, D. Essentials of econometrics. New York: McGraw-Hill, 1992. ISBN 0-13-844143-X. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: Přednáška 12 HOD/SEM.
Teacher's information
https://elearning.opf.slu.cz/course/view.php?id=1069
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016, Winter 2017, Winter 2019, Winter 2020, Winter 2021, Winter 2022, Winter 2023, Winter 2024.
  • Enrolment Statistics (Winter 2018, recent)
  • Permalink: https://is.slu.cz/course/opf/winter2018/INMNKSTZ