BA_PrSt Probability and Statistics

University of Finance and Administration
Winter 2021
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Hana Lipovská, Ph.D. (lecturer)
PaedDr. Renata Majovská, PhD. (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
BA_PrSt/cECMCPH: each odd Thursday 15:45–16:29 E228, each odd Thursday 16:30–17:15 E228, R. Majovská
BA_PrSt/pECMCPH: Mon 10:30–11:14 E228, Mon 11:15–12:00 E228, H. Lipovská
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, Data sets, Branches of Statistics
  • 2. Data classification, Data collection and design of a statistical study
  • 3. Descriptive Statistics: Data processing of verbal variable
  • 4. Data processing of numerical variable (a small data set), Measures of central tendency, variability, Quantiles
  • 5. A frequency distribution, Intervals
  • 6. Location characterizing of values of numerical variable (a large data set)
  • 7. Variability characterizing of values of numerical variable (a large data set), The Empirical rule and Testing for Normality, Chebyshev’s theorem
  • 8. Concentration characterizing of values of numerical variable
  • 9. Bivariate data, Linear correlation, Linear regression
  • 10. Probability: Probability of an event, empirical, theoretical, subjective probability, Properties of probability numbers, Law of large numbers
  • 11. Conditional probability, Rules of probability, Mutually exclusive events, Independent events
  • 12. Random variable, a distribution of discrete type
Literature
    required literature
  • Mario F. Triola. Elementary Statistics. Boston: Addison Wesley, 2003. 864 s. ISBN 0-8053-0271-9.
  • Hossein Pishro-Nik. Introduction to Probability, Statistics, and Random Processes. New York: Karra Research LLC, 2014. 731 s. ISBN 978-0990637202.
  • ZÁŠKODNÝ, Přemysl. The Principles of Probability and Statistics (Data Mining Approach). Praha: Curriculum, 2012, 122 pp. ISBN 978-80-904948-6-2. info
    recommended 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
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
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
English
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 2022.
  • Enrolment Statistics (Winter 2021, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/winter2021/BA_PrSt