INMNPSTZ Statistical Data Processing

School of Business Administration in Karvina
Winter 2026
Extent and Intensity
2/1/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Bartl, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Timetable
Mon 12:15–13:50 VS
  • Timetable of Seminar Groups:
INMNPSTZ/01: Mon 10:35–11:20 B101, R. Krkošková
INMNPSTZ/02: Mon 11:25–12:10 B101, R. Krkošková
INMNPSTZ/03: Tue 9:45–10:30 B101, R. Krkošková
INMNPSTZ/04: Tue 8:55–9:40 B101, R. Krkošková
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.

The capacity limit for the course is 500 student(s).
Current registration and enrolment status: enrolled: 209/500, only registered: 3/500
fields of study / plans the course is directly associated with
there are 7 fields of study the course is directly associated with, display
Course objectives
In connection with the Bachelor's degree Statistics course, or another basic Bachelor's degree statistics course, provide an explanation of other concepts of mathematical statistics, the main findings of this theory and basic statistical and econometric methods. Present the material with regard to applications in the economic field. Acquire appropriate manual computing skills and learn to solve statistical problems using Excel and the GRETL program on a computer.
Learning outcomes
The student can statistically process data using the method of simple/multiple linear regression, can perform simple nonlinear regression, can perform a statistical test of the influence of a factor on the expected value of a quantity using the method of one-factor analysis of variance (ANOVA), can perform a statistical test of the influence of a factor on the expected value of a quantity and a statistical test of the existence of an interaction between factors using the method of two-factor analysis of variance (ANOVA), can perform an analysis and predict the development of a time series.
Syllabus

1. Analysis of Variance – One Factor


Independent and dependent factor, assumptions of one-factor analysis of variance. Tightness of dependence, determination and correlation ratio.


2. Analysis of Variance – Two and more factors


Analysis of Variance with two factors. Assumptions of ANOVA with 2 factors. Two-factor ANOVA without interaction and with interaction. Kruskal-Wallis nonparametric ANOVA.


3. Regression analysis – One-way linear regression


What is regression analysis - simple, multiple, linear, nonlinear. The essence of simple linear regression analysis - scatter plot, regression line, regression coefficients, goodness of fit, coefficient of determination, hypothesis tests, confidence intervals. Simple nonlinear regression analysis - basic types of nonlinearity, Törnqvist curves and their applications in economics.


4. Regression analysis - Multivariate


Multiple linear regression analysis - assumptions, regression hyperplane, coefficient of determination. Application to examples from the economic field (marketing research). Classical multivariate linear regression model. Multicollinearity and its causes. Heteroskedasticity, H-S tests (Park's test, Bartley's test) and its removal. Autocorrelation (sign test).


5. Time series analysis


Types of economic time series. Elementary characteristics of time series. Economic time series models - decomposition method, exponential smoothing, ARIMA models. Analytical methods for determining time series trends: regression analysis (Least squares method, Maximum likelihood method). Synthetic methods: moving averages, exponential smoothing. Analysis of the seasonal component: models of constant seasonality with a step trend, with a linear trend. Proportional seasonality models. Random component analysis: statistical tests of the random component using residuals.


6. ARIMA models and time series forecasting


Stochastic process and its stationarity. Basics of ARIMA models: AR, MA, I, ARIMA models. Identification of ARIMA model using autocorrelation function (ACF) and partial autocorrelation function (PACF). Calculation of ARIMA model coefficients, model verification, prediction in ARIMA model.

Literature
    recommended literature
  • THRANE, Christer. Applied Regression Analysis: Doing, Interpreting and Reporting. 1st Edition. Routledge, 2020, 202 pp. ISBN 978-1-138-33547-9. info
  • GIBILISCO, Stan. Statistika bez předchozích znalostí. Brno: Computer Press, 2009, 272 pp. ISBN 978-80-251-2465-9. info
  • SEDLAČÍK, M., J. NEUBAUER a O. KŘÍŽ. Základy statistiky. 2. vyd. Praha: Grada, 2016. ISBN 978-80-247-5786-5. info
  • ANDERSON, David; Dennis J. SWEENEY; Thomas A. WILLIAMS; Jeffrey D. CAMM; James J. COCHRAN; James FREEMAN and Eddie SHOESMITH. Statistics for Business and Economics. 5th Edition. Cengage, 2020. ISBN 978-1-4737-6845-1. info
  • ANDERSON, David; Dennis J. SWEENEY; Thomas WILLIAMS; Jeffrey D. CAMM; James J. COCHRAN; Michael J. FRY and Jeffrey W. OHLMANN. Essentials of Modern Business Statistics with Microsoft® Excel®. 8th Edition. Cengage, 2020. ISBN 978-0-357-56952-8. info
  • BUDÍKOVÁ, Marie; Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami. První vydání. Praha: Grada, 2010. ISBN 978-80-247-3243-5. info
  • HANOUSEK, Jan and Pavel CHARAMZA. Moderní metody zpracování dat - matematická statistika pro každého. Praha: Grada, 1992. Educa '99. ISBN 80-85623-31-5. info
  • HYNDMAN, Rob J. and George ATHANASOPOULOS. Forecasting: Principles and Practice. OTexts, 2021. ISBN 978-0-9875071-3-6. URL info
  • KELLER, Gerald and Nicoleta GACIU. Statistics for Management and Economics. 2nd Edition. Cengage, 2019. ISBN 978-1-4737-6826-0. info
  • WALKER, Ian. Výzkumné metody a statistika. Praha: Grada, 2013. ISBN 978-80-247-3920-5. info
  • BRASE, Charles Henry; Corrinne Pellillo BRASE; Jason Mark DOLOR and James Allen SEIBERT. Understandable Statistics: Concepts and Methods. 13th Edition. Cengage, 2022. ISBN 978-0-357-71917-6. info
  • ZVÁRA, Karel. Regresní analýza. Praha: Academia, 1989. ISBN 80-200-0125-5. info
  • RAMÍK, Jaroslav and Šárka ČEMERKOVÁ. Statistika A. 3. rozšířené a upravené. Karviná: Slezská univerzita v Opavě, Obchodně podnikatelská fakulta v Karviné, 2000. ISBN 80-7248-097-9. 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
  • GUJARATI, Damodar N. Essentials of Econometrics. Fifth Edition. SAGE Publications, 2023, 632 pp. ISBN 978-1-0718-5039-8. info
  • RAMÍK, Jaroslav and Šárka ČEMERKOVÁ. Statistika B. 2. rozšířené a upravené. Karviná: Slezská univerzita v Opavě, Obchodně podnikatelská fakulta v Karviné, 2000. ISBN 80-7248-099-5. info
  • HINDLS, Richard; Markéta ARLTOVÁ; Stanislava HRONOVÁ; Ivana MALÁ; Luboš MAREK; Iva PECÁKOVÁ and Hana ŘEZANKOVÁ. Statistika v ekonomii. [Průhonice]: Professional Publishing, 2018, 395 pp. ISBN 978-80-88260-09-7. info
Teaching methods
lectures and seminars (exercises, examples and case studies)
Assessment methods
Assessment: participation in seminars (70%), midterm tests (30% of the assessment), final test (70% of the assessment)
Language of instruction
Czech
Teacher's information
The course includes acquiring relevant computing skills. During the 5th to 12th teaching weeks, students take 3 short written tests, the results of which are included in the overall exam result. At least 70% attendance in seminars is required.
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016, Winter 2017, Winter 2018, Winter 2019, Winter 2020, Winter 2021, Winter 2022, Winter 2023, Winter 2024, Winter 2025.
  • Enrolment Statistics (recent)
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