INMBKSME Statistical Methods for Economists

School of Business Administration in Karvina
Winter 2024
Extent and Intensity
16/0/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Jiří Mazurek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Jiří Mazurek, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Timetable
Sat 19. 10. 8:05–9:40 B308, Sat 9. 11. 8:05–9:40 B308, Sat 30. 11. 8:05–9:40 B308
Prerequisites
FAKULTA(OPF) && TYP_STUDIA(B) && FORMA(K) && !NOWANY( INMNASDP Statistical Data Processing || INMNASTZ Statistical Data Processing )
There are no prerequisites to pass this course. This course can be enrolled independently of other courses.
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 50 student(s).
Current registration and enrolment status: enrolled: 11/50, only registered: 0/50
fields of study / plans the course is directly associated with
Course objectives
To provide a deeper insight into the statistical methods suitable for multivariate data analysis. To grasp the theory of selected methods in order to apply these methods by using the computer and statistical software. To learn the principles of special mathematical methods applied in economics, which are an indispensable part of modern management. To learn to apply these methods practically in marketing and in efficient production quality control in all stages of the production process, i.e. in the pre-production, production and post-production stage.
Syllabus
  • 1. Elementary statistical concepts and methods
    2. Hypothesis testing in marketing – parametric tests
    3. Hypothesis testing in marketing – non-parametric tests
    4. Regression analysis (multiple)
    5. Correlation analysis
    6. Methods of sales prediction – fundamentals of time series
    7. Analysis of variance (ANOVA): one factor, two factors
    8. Analysis of variance (ANOVA): three factors (Latin squares), four factors (Graeco-Latin squares)
    9. Full experimental plans – full factorial design with two-level factors
    10. Fractional experimental plans – fractional factorial design with two-level factors
    11. Taguchi’s methods: loss function
    12. Taguchi’s methods: total quality costs

    1. Elementary statistical concepts and methods: Measures of central tendency. Measures of variability. Measures of data concentration (skewness, kurtosis). Statistical sample with two statistical variables.
    2. Hypothesis testing in marketing – parametric tests: Introduction to statistical testing, general outline of a statistical test, p-value of the test. Parametric tests: t-test for the mean value (one-sample, paired, two-sample), two-sample F-test for the equality of variances.
    3. Hypothesis testing in marketing – non-parametric tests: Sign test for the median (one-sample, paired). Pearson’s χ²-test for the goodness of fit. χ²-test of independence of qualitative data items. 4. Regression analysis: Introduction, simple linear regression, least squares method. Introduction to multiple linear regression, random vector, multivariate normal distribution. The classical assumptions of the linear regression analysis. The coefficient of determination (R²). Testing the significance of the regression coefficients, confidence intervals for regression coefficients, testing the model significance.
    5. Correlation analysis: Pearson’s correlation coefficient. Regression coefficient. Multiple correlation coefficient. Coefficient of partial correlation. Hypothesis testing. Non-parametric and robust methods: Spearman’s rank correlation coefficient. Multivariate linear dependence – relations for two explanatory variables.
    6. Methods of sales prediction: Decomposition of the time series into the trend, seasonal, cyclic, and random component. Analysis of the trend component (trend linear, quadratic, polynomic, exponential and logarithmic, logistic, Gompertz). Analysis of the seasonal component, model of constant seasonality. Prediction, point and interval estimates. Prognosis, causal prognostic methods. Analysis of the random component, autocorrelation, testing the properties of the random component, Durbin-Watson test. Moving averages.
    7. Analysis of variance (ANOVA): One-way ANOVA as a model of linear regression, coefficient of determination (R²), F-test. Two-way ANOVA without interactions. Two-way ANOVA with interactions.
    8. Analysis of variance (ANOVA): Three-way ANOVA (Latin squares). Four-way ANOVA (Graeco-Latin squares).
    9. Full factorial design of experiments (two-level factors): Foundations of experiment making and applications, experimental procedure, factor effect, factor significance, testing factor significance, graphical assessment of factor effect, graphs of interactions, modelling experiment as a model of linear regression.
    10. Fractional factorial design of experiments (two-level factors): Basic principles. One half fraction design. One quarter fraction design. One eighth fraction design. Graphical evaluation of factor effect in fractional plans.
    11. Taguchi’s methods: loss function: Definition and properties of loss function, loss function for various types of tolerance.
    12. Taguchi’s methods: total quality costs: Monitoring quality costs, control charts.
Literature
    required literature
  • RAMÍK, J., STOKLASOVÁ, R., TOŠENOVSKÝ, J. Statistické metody pro ekonomy. Karviná: OPF SU, 2003. ISBN 80-85943-63-8. info
    recommended literature
  • DANIEL, W. W., TERREL, J. Business statistics for management and economics. Houghton Mifflin, 2005. ISBN 978-03957-280-25. info
  • BLECHARZ, P. Základy metody DOE (Taguchiho přístup). Ostrava : REPRONIS, 2005. ISBN 80-7329-106-1. info
  • TOŠENOVSKÝ, J. Průvodce hodnocením způsobilosti. Ostrava, 2003. info
  • TOŠENOVSKÝ J., NOSKIEVIČOVÁ, D. Statistické metody pro zlepšování jakosti. Ostrava : Montanex, 2000. ISBN 80-7225-040-X. info
Teaching methods
One-to-One tutorial
Skills demonstration
Assessment methods
Written exam
Written test
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: Přednáška 16 HOD/SEM.
Teacher's information
A written test during the semester, written exam test.
ActivityDifficulty [h]
Consultations6
Other study duties93
Lectures6
Exam40
Total145
The course is also listed under the following terms Winter 2014, Winter 2015, Winter 2016, Winter 2017, Winter 2018, Winter 2019, Winter 2020, Winter 2022, Winter 2023.
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