MMEPSMEA Statistical Methods for Economists

School of Business Administration in Karvina
Winter 2013
Extent and Intensity
2/1/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Josef Tošenovský, CSc. (lecturer)
Ing. Elena Mielcová, Ph.D. (seminar tutor)
Ing. Filip Tošenovský, Ph.D. (seminar tutor)
prof. RNDr. Josef Tošenovský, CSc. (seminar tutor)
Guaranteed by
prof. RNDr. Josef Tošenovský, CSc.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Prerequisites (in Czech)
K absolvování předmětu nejsou vyžadovány žádné podmínky a předmět může být zapsán nezávisle na jiných předmětech.
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 (in Czech)
The course provides a greater insight into statistical methods used for multivariate data analysis. It presents selected methods and shows how to apply them with a support of statistical software. It also familiarizes students with some special economic-mathematical methods which are an essential part of modern management. Students learn the methods in a practical way, so that they can use them in marketing. Studying the methods, the students will also be able to control production quality at all stages of production processes. At the same time, they will learn methods which form an essential part of management education, as dictated by the internationally - recognized quality norms, such as QS 9000 from the U.S. or VDA 6.1. from Germany. These norms are compulsory for many industrial branches including all car industry suppliers.
Syllabus (in Czech)
  • 1. Introduction to statistical terms and methods
    2. Hypothesis testing in marketing - parametric tests
    3. Hypothesis testing in marketing - non-parametric tests
    4. Regression analysis
    5. Correlation analysis
    6. Analysis of variance (ANOVA)
    7. Analysis of variance (ANOVA) : two factors and Latin squares
    8. Full factorial plans
    9. Two-level fractional factorial plan
    10. Taguchi's methods - loss function
    11. Taguchi's methods - overall quality costs
    12. Sale forecasting methods
    1. Introduction to statistical terms and methods
    Location and variability characteristics, skewness, kurtosis, statistical sample with two variables, hypothesis testing
    2. Hypothesis testing in marketing - parametric tests
    A marketing case study enabling to test parameters of interest, one-sample t-test, two-sample t-test paired and unpaired.
    3. Hypothesis testing in marketing - non-parametric tests
    One-sample median test, one-sample Chi-squared test, two-sample tests, two-sample Chi-squared test, Mann-Whitney test, Wilcoxon paired test.
    4. Regression analysis
    The idea of regression, estimation of regression coefficients, test of significance for regression coefficients, confidence intervals for regression coefficients, goodness-of-fit test in regression.
    5. Correlation analysis
    Correlation coefficient, correlation index, Spearman's rank correlation coefficient, multivariate linear dependence - equations for two independent variables.
    6. Analysis of variance (ANOVA)
    One-factor ANOVA procedures and a measure of dependence.
    7. Analysis of variance (ANOVA) : two factors and Latin squares
    Two factors and three-factors (Latin squares) in ANOVA.
    8. Full factorial plans
    Introduction to statistical experiments, experimental procedure, effect of a factor and test for its significance, graphical assessment of a factor effect, interaction plots, model for an experiment.
    9. Two-level fractional factorial plan
    Half-fractional factorial plans, graphical method.
    10. Taguchi's methods - loss function
    Definition and properties of the loss function, loss function for different types of tolerances.
    11. Taguchi's methods - overall quality costs
    Monitoring of quality costs, control charts.
    12. Sale forecasting methods
    Trend and seasonal analysis, model for constant seasons, residual analysis, testing of residual properties, forecasting, causal forecasting model.
Literature
    required literature
  • J. G. VAN MATRE, G.H. GILBREATH. Statistics for Business and Economics. Business Publications, 1987. info
    recommended literature
  • JIJU ANTONY. Design of Experiments for Engineers and Scientists. Butterworth-Heinemann, 2003. info
  • RANJIT K. ROY. A Primer on the Taguchi Method (Competitive Manufacturing Series). Society of Manufacturing Engineers, 1990. info
Teaching methods
Skills demonstration
Seminar classes
Assessment methods
Written exam
Written test
Language of instruction
English
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 2007, Summer 2008, Winter 2008, Summer 2009, Winter 2009, Summer 2010, Winter 2010, Summer 2011, Winter 2011, Summer 2012, Winter 2012, Summer 2013, Summer 2014.
  • Enrolment Statistics (Winter 2013, recent)
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