B_PrSt Statistics and Probability

University of Finance and Administration
Winter 2022
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Jana Galvis Rivera (seminar tutor)
Ing. Hana Lipovská, Ph.D. (seminar tutor)
PaedDr. Renata Majovská, PhD. (seminar tutor)
doc. Radim Valenčík, CSc. (seminar tutor)
Guaranteed by
PaedDr. Renata Majovská, PhD.
Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Contact Person: Ivana Plačková
Timetable of Seminar Groups
B_PrSt/cAPH: each odd Wednesday 15:45–16:29 E228, each odd Wednesday 16:30–17:15 E228, R. Majovská
B_PrSt/cEKKV: Wed 12. 10. 10:30–11:14 KV204, 11:15–12:00 KV204, 12:15–12:59 KV204, 13:00–13:45 KV204, Wed 9. 11. 10:30–11:14 KV204, 11:15–12:00 KV204, 12:15–12:59 KV204, 13:00–13:45 KV204, Wed 7. 12. 10:30–11:14 KV204, 11:15–12:00 KV204, 12:15–12:59 KV204, 13:00–13:45 KV204, H. Lipovská
B_PrSt/cEKPH: each even Monday 14:00–14:44 E304, each even Monday 14:45–15:30 E304, H. Lipovská
B_PrSt/cFPH: each even Wednesday 15:45–16:29 E228, each even Wednesday 16:30–17:15 E228, R. Majovská
B_PrSt/cMMO: Thu 6. 10. 12:15–12:59 M26, 13:00–13:45 M26, 14:00–14:44 M26, 14:45–15:30 M26, Thu 3. 11. 12:15–12:59 M26, 13:00–13:45 M26, 14:00–14:44 M26, 14:45–15:30 M26, Thu 1. 12. 12:15–12:59 M26, 13:00–13:45 M26, 14:00–14:44 M26, 14:45–15:30 M26, R. Valenčík
B_PrSt/cMPH: each even Thursday 8:00–8:44 E004, each even Thursday 8:45–9:29 E004, except Thu 15. 12. ; and Tue 10. 1. 14:00–15:30 E225, J. Galvis Rivera
B_PrSt/pAFPH: Wed 14:00–14:44 E228, Wed 14:45–15:30 E228, R. Majovská
B_PrSt/poEKKV: each even Tuesday 17:30–18:14 KV204, each even Tuesday 18:15–19:00 KV204, each even Tuesday 19:15–19:59 KV204, each even Tuesday 20:00–20:45 KV204, except Tue 13. 12. ; and Mon 9. 1. 8:45–10:15 KV204, 10:30–12:00 KV204, J. Galvis Rivera
B_PrSt/poMMO: each even Tuesday 17:30–18:14 M17, each even Tuesday 18:15–19:00 M17, each even Tuesday 19:15–19:59 M17, each even Tuesday 20:00–20:45 M17, except Tue 13. 12. ; and Mon 9. 1. 8:45–10:15 M17, 10:30–12:00 M17, J. Galvis Rivera
B_PrSt/pxEKMPH: each even Tuesday 17:30–18:14 E230, each even Tuesday 18:15–19:00 E230, each even Tuesday 19:15–19:59 E230, each even Tuesday 20:00–20:45 E230, except Tue 13. 12. ; and Mon 9. 1. 8:45–10:15 E004, 10:30–12:00 E004, J. Galvis Rivera
B_PrSt/vAPH: Sat 1. 10. 14:00–15:30 E306, 15:45–17:15 E306, Fri 14. 10. 17:30–19:00 E303PC, 19:15–20:45 E303PC, Fri 4. 11. 14:00–15:30 E303PC, 15:45–17:15 E303PC, R. Majovská
B_PrSt/vEKFPH: Sat 22. 10. 9:45–11:15 E230, 11:30–13:00 E230, Sat 26. 11. 9:45–11:15 E306, 11:30–13:00 E306, Sat 10. 12. 14:00–15:30 E306, 15:45–17:15 E306, R. Majovská
Prerequisites
There are no prerequisites for this course.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
After completing the course students will be able to use basic terms of descriptive statistics. They will understand the problems of statistical surveys and will be able to process data. They will know the differences between simple and interval frequency distribution and they will also recognizes, in which case the simple frequency distribution is used for representation of the frequency distribution and in which case the interval frequency distribution (including graphical representation of the frequency distribution). Students will be able to measure the location, variability, skewness and kurtosis of frequency distribution.
They will be able to use the basic terms of probability theory, calculate the empirical and theoretical probability, create tree diagrams, Venn diagrams, use properties of probability numbers, the Law of large numbers. In the field of random variable, students will know some discrete probability distributions.
Learning outcomes
After completing the course, a student will be able to:
- process and analyse quantitative and qualitative data
- find characteristics of location and variability, coefficient of correlation
- calculate probability.
Syllabus
  • 1. Definition of Statistics, history of Statistics, Data classification, Data collection and design of a statistical study
  • 2. Descriptive Statistics: Data processing of verbal variable
  • 3. Data processing of numerical variable (a small data set), Measures of central tendency, variability
  • 4. Skewness, kurtosis, quantiles, Box plot
  • 5. A frequency distribution, Intervals
  • 6. Measures of central tendency and variability (a large data set)
  • 7. Skewness, kurtosis (a large data set), The Empirical rule and Testing for Normality, Chebyshev’s theorem
  • 8. Linear correlation, Linear regression
  • 9. Probability: Probability of an event, empirical, theoretical, subjective probability, Properties of probability numbers, Law of large numbers
  • 10. Conditional probability, rules of probability, mutually exclusive events, Independent events
  • 11. Random variable, probability and distribution function, characteristics of random variables
  • 12. Examples of distributions of discrete and continuous type, central limit theorem
Literature
    required literature
  • BÍLKOVÁ, Diana, Petr BUDINSKÝ and Václav VOHÁNKA. Pravděpodobnost a statistika (Probability and Statistics). 1. vydání. Plzeň: Vydavatelství a nakladatelství Aleš Čeněk, s.r.o, 2009, 658 pp. ISBN 978-80-7380-224-0. info
  • ŘEZANKOVÁ, Hana, Tomáš LÖSTER a Zdeněk ŠULC. Úvod do statistiky. Vydání 2. přepracované. Praha: Oeconomica, 2019. Vysokoškolská skripta. ISBN 978-80-245-2301-9.
  • NEUBAUER, Jiří, Marek SEDLAČÍK a Oldřich KŘÍŽ. Základy statistiky: aplikace v technických a ekonomických oborech. 3., rozšířené vydání. Praha: Grada Publishing, 2021. ISBN 978-80-271-3421-2.
    recommended literature
  • KOŠŤÁKOVÁ, Tereza. O složitém jednoduše, aneb, Nebojte se statistiky, nekouše. Ilustroval Tomáš ZIMA. Praha: Český statistický úřad, 2019. ISBN 978-80-250-2908-4.
  • SVOBODA, Milan, Mikuláš GANGUR a Kateřina MIČUDOVÁ. Statistické zpracování dat. Plzeň: Západočeská univerzita v Plzni, 2019. ISBN 978-80-261-0883-2.
  • ARLTOVÁ, Markéta, Diana BÍLKOVÁ and ET AL. Sbírka příkladů ze statistiky. Statistika A. Praha: Oeconomica, 2000, 272 pp. ISBN 80-7079-727-4. info
Teaching methods
Lectures and seminars in full-time study; tutorials in part-time study, MS Excel will be presented to the students. Minimum mandatory attendance at seminars in full-time is 75 %, the controlled group consultations in combined studies 50 %. Students who fail to meet the mandatory level of participation can be given additional study obligations. Students with ISP have the same duties. Compulsory attendance is not required.
Assessment methods
Course ending:
Credit:
- 1 written test, it is necessary to obtain at least 50 % of points from the test;
- 1 semester work from the area of descriptive statistics, it is needed to obtain at least 75 % of points;
Exam:
- 1 written test, it is necessary to obtain at least 60 % of points from the test;
- oral examination: student will answer 2 theoretical questions;
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: 12 hodin KS/semestr.
The course is also listed under the following terms Winter 2016, Winter 2017, Winter 2018, Winter 2019, Winter 2020, Winter 2021, Winter 2023, Winter 2024.
  • Enrolment Statistics (Winter 2022, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/winter2022/B_PrSt