INMBPSTA Statistics

School of Business Administration in Karvina
Summer 2018
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
prof. RNDr. Jaroslav Ramík, CSc. (lecturer)
Mgr. Radmila Krkošková, Ph.D. (seminar tutor)
doc. Mgr. Jiří Mazurek, Ph.D. (seminar tutor)
Ing. Elena Mielcová, Ph.D. (seminar tutor)
Ing. Zuzana Neničková, Ph.D. (seminar tutor)
Ing. Radomír Perzina, Ph.D. (seminar tutor)
prof. RNDr. Jaroslav Ramík, CSc. (seminar tutor)
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.
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
The course objective is to teach the students the principles of mathematical and economical statistics. The course acquaints students with the basic mathematical and statistical methods of data analysis with respect to real applications in economy. The course follows after the basic courses of calculus and information science. The student should acquire also appropriate calculation skills and should be able to solve statistical problems by Excel on PC. It leads to the creating of the essential profile of all students at the School of Business Administration. At the same time it is the base of the university education of further economic courses on the bachelor's as well as the master's study.
Syllabus
  • 1. Statistics and its importance
    2. Descriptive statistics - quantitative and qualitative variables
    3. Elements of probability
    4. Random variable
    5. Discrete probability models
    6. Continuous probability models
    7. Point estimation
    8. Confidence intervals
    9. Hypotheses testing - parametric tests
    10. Hypotheses testing - non-parametric tests
    11. Analysis of variance - ANOVA
    12. Simple regression analysis
    1. Statistics and its importance
    Statistical methods in business and entrepreneurship, descriptive and inductive statistics, statistics in decision making.
    2. Descriptive statistics - quantitative and qualitative variables
    Qualitative and quantitative variables, frequency distribution, characteristics of central tendency, characteristics of variation, variance, standard deviation.
    3. Elements of probability
    Random event, combinatorics, intuitive definition of probability, probability as a relative frequency, properties of probability.
    4. Random variable
    Discrete and continuous random variable, characteristics of random variable, characteristics of central tendency and variation (mean, variance and standard deviation)
    5. Discrete probability models
    Probability function, distribution function, uniform distribution, binomial distribution, Poisson distribution, other well known discrete distributions.
    6. Continuous probability models
    Density function, uniform distribution, normal distribution, lognormal distribution, exponential distribution other discrete distributions.
    7. Point estimation
    Point estimation and its properties.
    8. Confidence intervals
    Interval estimation and its properties, confidence intervals for the mean, variance and ratio.
    9. Hypotheses testing - parametric tests
    Statistical testing, kinds of hypotheses, one-sided and two-sided tests, test for the mean value, and the variance.
    10. Hypotheses testing - non-parametric tests
    Chi-square distribution, chi-square tests of goodness of fit, test of independence in the contingence tables.
    11. Analysis of variance - ANOVA
    One-way ANOVA, Fisher's distribution F, F-test for the mean, two-way ANOVA.
    12. Simple regression analysis
    Regression linear model, least squares method, linear regression function, prediction in time series.
Literature
    required literature
  • RAMÍK, J., ČEMERKOVÁ, Š. Kvantitativní metody B - Statistika. Distanční studijní opora. Karviná, OPF SU, 2003. ISBN 80-7248-198-3. info
    recommended literature
  • ANDĚL, J. Základy matematické statistiky. Praha : Matfyzpress, 2011. ISBN 978-80-7378-162-0. info
  • HINDLS, R. Statistika pro ekonomy. Praha : Professional Publishing, 2007. ISBN 978-80-86946-43-6. info
  • MAREK,L. a kol. Statistika pro ekonomy - aplikace. Praha : Professional Publishing, 2007. ISBN 978-80-86946-40-5. info
  • DANIEL, W. W., TERREL, J. Business statistics for management and economics. Houghton Mifflin, 2005. ISBN 978-03957-280-25. info
  • HINDLS, R., HRONOVÁ, S., NOVÁK, I. Metody statistické analýzy pro ekonomy. Praha : Management Press, 2000. ISBN 80-7261-013-9. info
  • RAMÍK, J., ČEMERKOVÁ, Š. Statistika pro ekonomy. Karviná: OPF SU, 2000. info
  • VAN MATRE, J. G., GILBREATH, G. H. Statistics for Business and Economics. BPI/IRWIN, Homewood, 1997. ISBN 0-256-03719-1. info
Teaching methods
Skills demonstration
Seminar classes
Assessment methods
Written exam
Written test
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
https://elearning.opf.slu.cz/course/view.php?id=745
test, exam test
ActivityDifficulty [h]
Ostatní studijní zátěž91
Přednáška26
Seminář13
Zkouška40
Summary170
The course is also listed under the following terms Summer 2015, Summer 2016, Summer 2017, Summer 2019, Summer 2020.
  • Enrolment Statistics (Summer 2018, recent)
  • Permalink: https://is.slu.cz/course/opf/summer2018/INMBPSTA