INMBKKME Quantitative Methods in Economic Practice

School of Business Administration in Karvina
Winter 2026
Extent and Intensity
16/0/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Radmila Krkošková, Ph.D. (lecturer)
Guaranteed by
Mgr. Radmila Krkošková, Ph.D.
Department of Informatics and Mathematics – School of Business Administration in Karvina
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Timetable
Sat 17. 10. 9:45–11:20 VS, Sat 21. 11. 9:45–11:20 VS, Sat 19. 12. 9:45–11:20 VS
Prerequisites (in Czech)
FAKULTA(OPF) && TYP_STUDIA(B) && FORMA(K) && !NOWANY(INMBAKVM Quantitave Methods)
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: 0/500, only registered: 0/500
fields of study / plans the course is directly associated with
Course objectives
The aim 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 aim is to apply the acquired knowledge in practice.
Learning outcomes
The student can define the linear space of a matrix, knows the rules for matrix operations, understands the calculation of determinants and their use, is able to apply Cramer's rule for crossing systems of linear equations, is able to distinguish and define arithmetic and geometric sequences, understands the concepts of convergent and divergent sequences, can define real functions of one real variable and their properties, knows graphs of elementary functions, understands the concept of the derivative of a function and is able to analyze the course of a function using differential calculus, can calculate position characteristics (mode, median, quantiles, averages) and variability characteristics (variance, standard deviation, range), understands probability models and can apply them in practice, understands the procedure for testing hypotheses, can explain the meaning of the significance level and p-value, is able to calculate the goodness-of-fit test and test the independence of qualitative features, understands simple regression analysis, can use and explain the least squares method, is able to interpret the coefficient of determination and its meaning in 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 form, rank of a matrix. Unit matrix, regular and singular matrix. Product of matrices and its properties. Inverse matrix. Solution of matrix equations. Calculation of determinant. Determinant of regular and singular matrix. Cramer's rule. Calculation of inverse matrix.


2. Sequence and limit of sequence


Arithmetic and geometric sequence. Finite and infinite sequence. Bounded and unbounded sequence. Monotone sequence. Convergent and divergent sequence. Calculation of limit of sequence, properties of limit of sequences.


3. Functions of one real variable and its limit


Real functions of one real variable. Supremum and infimum, bounded, monotone, convex and concave function. Simple function and inverse function. Elementary functions. Domain of definition of elementary functions, their properties and graphs. Continuity of a function of one real variable and its properties. Bolzano and Weierstrass theorems. Limit of a function. Asymptotes of a function. Theorems on limits of a function.


4. Differential calculus of a function of one real variable


Derivative of a function given explicitly, geometric meaning of the derivative, relation of continuity and proper derivative. Theorem on the derivative of arithmetic operations, on the derivative of a composite function. Differential, higher-order derivatives. Investigation of the course of a function.


5. Descriptive statistics - qualitative and quantitative characteristics


Statistical unit and statistical set. Frequency distribution of qualitative characteristics. Frequency distribution of quantitative characteristics. Characteristics of position (mode, median, quantiles, means). Characteristics of variability (variance, standard deviation, range). Coefficient of variation.


6. Discrete and continuous probability models


Uniform distribution. Binomial distribution. Poisson distribution. Normal distribution. Exponential distribution. Chi-square distribution. Student's distribution.


7. Hypothesis testing - parametric and nonparametric tests


Basic concepts of hypothesis testing. Procedure for hypothesis testing. Significance level and p-value of the test. Two-sample tests. Goodness of fit test (Chi-square test). Testing the independence of qualitative characteristics.


8. Simple regression analysis


Statistical dependence between two quantitative characteristics. Simple linear regression. Least squares method. Classical linear model. Coefficient of determination.

Literature
    required literature
  • RAMÍK, J. a Š. ČEMERKOVÁ. Kvantitativní metody B - Statistika. Karviná: SU OPF, 2003. ISBN 80-7248-198-3. info
  • STOKLASOVÁ, R. Kvantitativní metody. Karviná: SU OPF, 2013. ISBN 978-80-7248-848-3. info
    recommended literature
  • ANDĚL, J. Základy matematické statistiky. Praha : Matfyzpress, 2011. ISBN 978-80-7378-162-0. 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
  • 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
  • KLŮFA, J. a J. COUFAL. Matematika 1. Praha: Ekopress, 2003. ISBN 8086119769. info
  • KAŇKA, M. Sbírka řešených příkladů z matematiky pro studenty vysokých škol. Praha: Ekopress, 2009. ISBN 978-80-86929-53-8. info
  • ARLTOVÁ, M. a kol. Základy statistiky v příkladech. Tribun EU s.r.o., 2014. ISBN 978-80-2630-756-3. info
Teaching methods
Lectures, group projects.
Assessment methods
assessment: final exam test - written form
Language of instruction
Czech
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is also listed under the following terms Winter 2018, Winter 2019, Winter 2020, Winter 2021, Winter 2022, Winter 2023, Winter 2024, Winter 2025.
  • Enrolment Statistics (Winter 2026, recent)
  • Permalink: https://is.slu.cz/course/opf/winter2026/INMBKKME