Course: Probability and Mathematical Statistics

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Course title Probability and Mathematical Statistics
Course code KMA/PMSZ
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course unspecified
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)
  • Volf Petr, doc. CSc.
  • Picek Jan, prof. RNDr. CSc.
Course content
1. Combinatorics 2. Probability theory: random events, the definition of probability, probability properties. 3. The independence of random events, conditional probability. Bayes theorem. 4. Descriptive Statistics: Types of variables, basic characteristics of location and variability. Ordered data, median, quantiles. Graphic data processing. 5. Random variable. Distribution function and its properties, density, quantile function. Characteristics of random variables. The law of large numbers. 6. Discrete random variables: alternative, binomial, negative binomial, hypergeometric, Poisson. 7. Continuous distributions: normal distribution, uniform, exponential, Weibull, Student and F distributions. The central limit theorem. 8. Multivariate random variable (vector), the dependence - covariance and correlation coefficient 9. Introduction to Mathematical Statistics. Point and interval estimates. 10. Basic concepts of statistical hypothesis testing. Tests of hypotheses on the parameters of the normal and binomial distribution. 11. One-way analysis of variance. Non-parametric tests. 12. Goodness of fit tests. 13. Correlation and regression. Spearman's coefficient of serial correlation. 14. Linear regression, method of least squares. Regression diagnostics.

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing)
  • Class attendance - 70 hours per semester
  • Preparation for credit - 80 hours per semester
Learning outcomes
Elements of probability theory, data analysis and statistics.
Basic knowledge of mathematical statistics and probability
Prerequisites
Basic knowledge of differential and integral calculus (first year)

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
  • Kadeřábek, J. - Picek, J. Sbírka příkladů z pravděpodobnosti a statistiky. Liberec : Technická univerzita v Liberci, 2001. ISBN 80-7083-454-4.
  • Kadeřábek J. Statistika. Liberec : Technická univerzita v Liberci, 2006. ISBN 80-7372-044-2.
  • Linka A., Picek J., Volf P. Úvod do teorie pravděpodobnosti.. Liberec: Technická univerzita v Liberci, 2001. ISBN 80-7083-453-6.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester