INMBPKME Quantitative Methods in Economic Practice

School of Business Administration in Karvina
Winter 2020
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
Ing. Hana Kováčková (seminar tutor)
Mgr. Radmila Krkošková, Ph.D. (seminar tutor)
Ing. Zuzana Neničková, Ph.D. (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.
Timetable
Mon 12:15–13:50 VS
  • Timetable of Seminar Groups:
INMBPKME/01: Mon 15:35–16:20 A217, H. Kováčková
INMBPKME/02: Mon 14:45–15:30 A217, H. Kováčková
INMBPKME/03: Mon 16:25–17:10 A217, H. Kováčková
INMBPKME/04: Thu 10:35–11:20 A501, H. Kováčková
INMBPKME/05: Thu 8:05–8:50 A501, R. Krkošková
INMBPKME/06: Thu 8:55–9:40 A501, R. Krkošková
INMBPKME/07: Thu 9:45–10:30 A501, H. Kováčková
Prerequisites (in Czech)
FAKULTA(OPF) && TYP_STUDIA(B) && 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 400 student(s).
Current registration and enrolment status: enrolled: 3/400, only registered: 0/400
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to understand basic concepts from higher mathematics (matrix calculus, functions of one real variable, differential calculus of functions of one real variable) and statistics (descriptive statistics, discrete and continuous probability models, hypothesis testing, regression analysis). The goal is to be able to apply the acquired knowledge in practice.
Learning outcomes
After completing the course, the student will be able to: analyze the course of a function (calculate the extrema of a function, determine its properties); write equations of elementary functions and will know their properties; apply descriptive statistics in practice; be able to to to use and interpret the results of linear regression analysis.
Syllabus
  • 1. Matrix calculus and determinants Basic concepts, sum of matrices and multiplication of matrices by a constant, linear space of matrices. Adjustment to triangular shape, matrix rank. Unit matrix, regular, and singular matrix. Product of matrices and its properties. Inverse matrix. Solving matrix equations. Calculation of the determinant. Determinant of regular and singular matrices. Cramer's rule. Calculation of the inverse matrix. 2. Sequence and limit of sequence Arithmetic and geometric sequence. A finite and infinite sequence. Bounded and unbounded sequence. A monotonous sequence. Convergent and divergent sequence. Calculation of sequence limits, and properties of sequence limits. 3. Functions of one real variable and its limit Real functions of one real variable. Supremum and infimum, limited function, monotonic, convex, and concave. Simple function and inverse function. Elementary function. Definition field of elementary functions, their properties, and graphs. Continuity of a function of one real variable and its properties. The theorem of Bolzano and Weierstrass. Function limit. Asymptotes of the function. Theorems about the limits of a function. 4. Differential calculus of a function of one real variable The derivative of a function is given explicitly, as the geometric meaning of the derivative, the relation of continuity, and proper derivatives. The theorem is about the derivative of arithmetic operations and the derivative of a complex function. Differential, derivatives of higher orders. Investigation of the progress of the function. 5. Descriptive statistics - qualitative and quantitative characteristics Statistical unit and statistical file. Frequency distribution of qualitative characters. Frequency distribution of quantitative traits. Location characteristics (mode, median, quantiles, averages). Characteristics of variability (dispersion, standard deviation, range). Coefficient of variation. 6. Discrete and continuous probabilistic models Uniform distribution. Binomial distribution. Poisson distribution. Normal distribution. Exponential distribution. Chi-square distribution. Student's distribution. 7. Hypothesis testing - parametric and non-parametric tests Basic concepts of hypothesis testing. Hypothesis testing procedure. Significance level and p-value of the test. Two-choice tests. The goodness of fit test (Chi-square test). Testing the independence of qualitative features. 8. Simple regression analysis Statistical dependence between two quantitative traits. Simple linear regression. The method of least squares. Classical linear model. Coefficient of determination.
Literature
    required literature
  • STOKLASOVÁ, R. Kvantitativní metody. Karviná: SU OPF, 2013. ISBN 978-80-7248-848-3. info
  • RAMÍK, J. a Š. ČEMERKOVÁ. Kvantitativní metody B - Statistika. Karviná: SU OPF, 2003. ISBN 80-7248-198-3. info
    recommended literature
  • HINDLS, R., S. HRONOVÁ, J. SEGER, a J. FISCHER. Statistika pro ekonomy. 8. vyd. 978-80-8694-643-6, 2016. ISBN 978-80-8694-643-6. info
  • SEDLAČÍK, M., J. NEUBAUER a O. KŘÍŽ. Základy statistiky. 2. vyd. Praha: Grada, 2016. ISBN 978-80-247-5786-5. info
  • MOUČKA, J. a P. RÁDL. Matematika pro studenty ekonomie. 2. vyd. Praha: Grada, 2015. ISBN 978-80-247-5406-2. info
  • ARLTOVÁ, M. a kol. Základy statistiky v příkladech. Tribun EU s.r.o., 2014. ISBN 978-80-2630-756-3. info
  • ANDĚL, J. Základy matematické statistiky. Praha : Matfyzpress, 2011. ISBN 978-80-7378-162-0. info
  • KAŇKA, M. Sbírka řešených příkladů z matematiky pro studenty vysokých škol. Praha: Ekopress, 2009. ISBN 978-80-86929-53-8. info
  • KLŮFA, J. a J. COUFAL. Matematika 1. Praha: Ekopress, 2003. ISBN 8086119769. info
Teaching methods
Lectures, group projects.
Assessment methods
written test, final written examination, 60% of correct answers is needed to pass examination
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Teacher's information
Student requirements: attendance at seminars, and mid-term test. Evaluation methods: attendance at seminars min. 60% (10% grade), 1 mid-term test (30% grade), written exam (60% grade).
The course is also listed under the following terms Winter 2018, Winter 2019, Winter 2021, Winter 2022, Winter 2023, Winter 2024.
  • Enrolment Statistics (Winter 2020, recent)
  • Permalink: https://is.slu.cz/course/opf/winter2020/INMBPKME