Course: Statistic for Management

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Course title Statistic for Management
Course code KMA/STA
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study 1
Frequency of the course každý rok
Semester Winter
Number of ECTS credits 6
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • Schindler Martin, Mgr. Ph.D.
  • Picek Jan, prof. RNDr. CSc.
  • Brzezina Miroslav, doc. RNDr. CSc., dr. h. c.
  • Slámová Tereza, Mgr. Ph.D.
Course content
Repetition of differential and integral calculus of functions of one variable. Differential calculus of functions of two or more variables: the concept of a function of n-variables, domain values??, graphs, partial derivatives. Local, global and constrained extrems. Integral calculus of functions of several variables: regions, the double integral and its properties, the calculation of double integrals (Fubini theorem). Matrix algebra, systems of linear equations. Probability - the basic properties and concepts: a random phenomenon, the definition of probability, conditional probability, independence of random events. Random variables: discrete random variable, continuous random variables, distribution functions, characteristics of random variables. Random Vector: distribution function, marginal distribution, independent random variables, conditional distribution, the characteristics of a random vector. Basic concepts of mathematical statistics: random sampling, point and interval estimation, consistency and unbiased estimate, basic hypothesis testing, analysis of variance. Linear regression model: the method of least squares, tests of regression parameters, regression diagnostics. Tests of two vectors of mean values ??(Hotelling tests). Methods of classification Discriminant analysis. Logistic regression. Cluster analysis

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing)
  • Class attendance - 70 hours per semester
  • Preparation for exam - 110 hours per semester
Learning outcomes
Knowledge of calculus - multidimensional functions and knowledge of advanced methods of mathematical statistics and probability theory
Basic knowledge of calculus - functions of several variables, ability to apply advanced methods of mathematical statistics and probability theory
Prerequisites
Mathematics I (MV1) and Mathematics II (MV2) relevant Bachelor's degree

Assessment methods and criteria
Oral exam, Written exam

Requirements on credit: two tests of the subject matter. The date of each test will be announced in advance by teacher. It is necessary to get score at least 50% for each test. Requirements on exam: Knowledge of problem solving, concepts and basic ideas.
Recommended literature
  • Anděl, J. Statistické metody. Matfyzpress: Praha, 2007. ISBN 978-80-7378-003-6.
  • Hebák, P., Hustopecký, J., Malá, I. Vícerozměrné statistické metody (2). Informatorium, Praha, 2005. ISBN 80-7333-036-9.
  • Meloun M., Militký J. Statistická analýza experimentálních dat. Praha : Academia, 2004. ISBN 80-200-1254-0.
  • Mezník, I. , Karásek, J., Miklíček, J.:. Matematika I pro strojní fakulty. SNTL, Praha, 1992.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Textile Engineering Study plan (Version): Product Engineering (2012) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Textile Engineering Study plan (Version): Quality Control (2012) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Textile Engineering Study plan (Version): Product Engineering (2012) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Textile Engineering Study plan (Version): Product Engineering (2012) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Textile Engineering Study plan (Version): Quality Control (12) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Textile Engineering Study plan (Version): Product Engineering (12) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter