INMNASTZ Statistical Data Processing

School of Business Administration in Karvina
Winter 2019
Extent and Intensity
2/1/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Elena Mielcová, Ph.D. (lecturer)
prof. RNDr. Jaroslav Ramík, CSc. (lecturer)
Ing. Elena Mielcová, Ph.D. (seminar tutor)
doc. RNDr. David Bartl, Ph.D. (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.
Timetable
Tue 10:35–12:10 A412
  • Timetable of Seminar Groups:
INMNASTZ/01: Tue 12:15–13:00 A412, D. Bartl, E. Mielcová
Prerequisites (in Czech)
FAKULTA ( OPF ) && TYP_STUDIA ( N ) && FORMA ( P )
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
Fields of study the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
The aim of this course is to provide students with the necessary knowledge in a comprehensive way with statistical methods suitable to process multidimensional and time series data. Absolvent of this course will be able to use an appropriate mathematical apparatus to multidimensional and time series data processing and apply appropriate statistical software on necessary statistical calculations.
Syllabus
  • Review of Basic Statistical Concepts
    Experiment, sample space, sample point, random variables. Sample and population characteristics. Graphical evaluation of data distribution. Estimation and hypothesis testing. Parametrical and non-parametrical hypothesis tests. Statistical software.
    Analysis of Variance (ANOVA)
    ANOVA and basic principles of experimental design. Analysis of variance with one factor and analysis of variance with two factors. Assumptions of ANOVA with two factors. Two-factor ANOVA without interaction and interaction. Kruskal-Wallis nonparametric ANOVA.
    Simple Linear Regression
    Definition of regression analysis - a simple, multiple, linear, nonlinear regression. Simple linear regression analysis - scatter diagram, regression, regression coefficients, adhesion, coefficient of determination, tests of hypotheses. Basic types of nonlinearities, Törnquist curves and their applications in economics.

    Multiple Linear Regression
    Basic terms in multiple linear regression analysis - criteria, predictors, regression hyperplane, the coefficient of determination. Using the regression analysis for nominal predictors and correlation coefficients. Application examples of the economic area (marketing research). Problems in regression analysis: multicollinearity, heteroscedasticity, autocorrelation
    Heteroscedasticity tests (Park test, Bartley test), autocorrelation test (sign test).
    Time Series Analysis
    Types of economic time series. Elemental characteristics of time series. Models of economic time series - decomposition, exponential smoothing, ARIMA. Analytical methods for the determination of trends of time series: regression analysis (least squares method, maximum likelihood method). Synthetic methods: moving averages, exponential smoothing. Analysis of seasonal and random components of time series. Exponential smoothing models of time series (simple, Holt, Winters model). 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.
Literature
    required literature
  • ASTERIOU, D., HALL, S. G. Applied Econometrics. 2nd ed. New York: Palgrave Macmillan, 2011. ISBN 0230271820. info
  • GUJARATI, D. N. and PORTER, D. C. Essentials of Econometrics. 4th ed. New York: McGraw-Hill/Irwin, 2010. ISBN 0073375845. info
    recommended literature
  • KELLER, G. Statistics for Management and Economics. Southwestern / Cengage Learning, 2014. ISBN 978-1285425450. info
  • HYNDMAN, R. J., ATHANASOPOULOS, G. Forecasting: Principles and Practice. www.otexts.org, 2013. ISBN 978-0987507105. URL info
  • ANDERSON, D. R., SWEENEY, D. J., and WILLIAMS, T. A. Essentials of modern business statistics with Microsoft Office Excel. 5th Ed. Mason. Ohio: South-Western / Cengage Learning, 2011. ISBN 0840062389. info
Teaching methods
Lecture with presentation in Power-point
Assessment methods
Grade
Written exam
Language of instruction
English
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
attendance in seminars 50 %, ongoing tests, final written exam
ActivityDifficulty [h]
Ostatní studijní zátěž61
Přednáška26
Seminář13
Zkouška40
Summary140
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016, Winter 2017, Summer 2018, Winter 2018.
  • Enrolment Statistics (recent)
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