B_ASt Applied Statistics

University of Finance and Administration
Winter 2024
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
Mgr. Jiří Henzl (seminar tutor)
Mgr. Petr Chlebek (seminar tutor)
Ing. Hana Lipovská, Ph.D. (seminar tutor)
Guaranteed by
Ing. Hana Lipovská, Ph.D.
Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Contact Person: Ivana Plačková
Timetable of Seminar Groups
B_ASt/cEKKV: Thu 17:30–18:14 KV206, except Thu 14. 11. ; and Thu 14. 11. 18:15–19:00 KV206, P. Chlebek
B_ASt/cEK1PH: each odd Monday 10:30–11:14 E227, each odd Monday 11:15–12:00 E227, H. Lipovská
B_ASt/cEK2PH: each even Monday 14:00–14:44 E227, each even Monday 14:45–15:30 E227, except Mon 25. 11. ; and Fri 6. 12. 8:45–10:15 E227, H. Lipovská
B_ASt/cMMO: each even Thursday 12:15–12:59 M22, each even Thursday 13:00–13:45 M22, except Thu 3. 10., except Thu 14. 11. ; and Thu 10. 10. 12:15–13:45 M22, Thu 19. 12. 12:15–13:45 M22, J. Henzl
B_ASt/cM1PH: each even Tuesday 10:30–11:14 E228, each even Tuesday 11:15–12:00 E228, H. Lipovská
B_ASt/cM2PH: each odd Tuesday 10:30–11:14 E227, each odd Tuesday 11:15–12:00 E227, H. Lipovská
B_ASt/pEKKV: Thu 15:45–16:29 KV206, Thu 16:30–17:15 KV206, except Thu 14. 11. ; and Thu 14. 11. 16:30–18:00 KV206, P. Chlebek
B_ASt/pEKPH: Mon 12:15–12:59 E004, Mon 13:00–13:45 E004, H. Lipovská
B_ASt/pMMO: each even Thursday 8:45–9:29 M22, each even Thursday 9:30–10:15 M22, each even Thursday 10:30–11:14 M22, each even Thursday 11:15–12:00 M22, except Thu 3. 10., except Thu 14. 11. ; and Thu 10. 10. 8:45–12:00 M22, Thu 19. 12. 8:45–12:00 M22, J. Henzl
B_ASt/pMPH: Tue 8:45–9:29 E004, Tue 9:30–10:15 E004, H. Lipovská
B_ASt/vEKPH: Fri 18. 10. 17:30–19:00 E228, 19:15–20:45 E228, Fri 1. 11. 17:30–19:00 E228, 19:15–20:45 E228, Fri 29. 11. 17:30–19:00 E228, 19:15–20:45 E228, H. Lipovská
Prerequisites
Basic knowledge of mathematics at the high school level.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim is to prepare the student for formulating and testing the hypotheses of seminar, bachelor's and diploma theses and familiarization with traditional software tools, data visualization tools and the implementation of AI.
Learning outcomes
After completing the course, the student will be able to interpret the results of statistical surveys and graphic displays. He will be able to prepare basic and advanced statistics, graphical and tabular outputs from data files. Can process data using advanced office software and present it in a neat and understandable form. He will be able to prepare assignments for the implementation of complex surveys and investigations. He will recognize misleading and incorrect interpretations of the results, he will be able to explain how to correct them using mathematical statistics. He will know position indicators and variability indicators, he will be able to work with absolute and relative frequency, compile, use and interpret contingency tables. He will be able to perform correlation analysis and interpret its results. With the help of the software, he will be able to perform least squares linear regression estimation, be able to interpret the results and be aware of the pitfalls of this approach. He will know the official data sources and look for the necessary statistics in them.
Syllabus
  • 1. Graphical display of data. 2. Official statistical databases, data sources and working with them. 3. Basic statistical concepts. 4. Word variable data processing. 5. Elementary processing of data on numerical variables, quantiles. 6. Characterizing the position of values of a numeric variable. 7. Characterizing the variability of the values of a numerical variable. 8. Correlation coefficient, interpretation, pitfalls of Pearson's correlation coefficient. 9. Basic errors in data interpretation. 10. p-value, its interpretation and pitfalls 11. Linear regression, method of least squares Coefficient of determination, its interpretation and pitfalls
Literature
    required literature
  • FIELD, A. P., Discovering Statistics Using SPSS: (and Sex and Drugs and Rock 'n' Roll). SAGE, 2020. ISBN 9789351500827. 915 p.
    recommended literature
  • Harford, T. 2020. How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers. Hachette UK. ISBN 9781408712221, 352 p.
  • Blauw, S. 2021. The Number Bias. Hodder And Stoughton Ltd. ISBN: 1529342775. 175 p
Teaching methods
Lectures, exercises, preparation of projects.
Assessment methods
Credit – the student submits a complete proposal of a statistical research method for a problem of his choice. He will defend his proposal in a presentation at the seminar. Exam: oral - the student a) interprets a graph from real practice, identifies its weak points and suggests improvements, b) answers 2 theoretical questions c) demonstrates the ability to work with a data set.
Language of instruction
Czech
Further comments (probably available only in Czech)
Information on the extent and intensity of the course: 12 hodin KS/semestr.
The course is also listed under the following terms Winter 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/winter2024/B_ASt