XASD Statistical Data Processing

Moravian Business College Olomouc
winter 2023

The course is not taught in winter 2023

Extent and Intensity
0/3/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Jan Wossala, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Jan Wossala, Ph.D.
Moravian Business College Olomouc
Supplier department: Moravian Business College Olomouc
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course introduces students to basic statistical concepts and the most important methods with an emphasis on understanding the meaning of statistical activities. The objective of the course is to create or, more precisely, to consolidate the basics of statistical literacy among the students as well as their understanding of statistical data and indicators and to familiarise them with basic statistical methods. After completing the course, the student: masters basic statistical processing of data set in the form of tables, graphs and numerical characteristics; understands basic probability concepts; is able to solve probability problems based on the explained theory; is able to generate realisation of selected types of random variables using statistical software, has knowledge of the basics of statistical data analysis and their presentation.
Syllabus
  • 1. Combinatorics.
  • 2. Probability phenomena.
  • 3. Random variables.
  • 4. Basic types of probability distributions of discrete random variables.
  • 5. Basic types of probability distributions of continuous random variables.
  • 6. Random vectors.
  • 7. Statistical data set with one statement.
  • 8. Statistical data set with two statements.
  • 9. Regression and correlation analysis.
  • 10. Time series.
  • 11. Inductive statistics.
  • 12. Testing of statistical hypotheses.
Literature
    required literature
  • MENDENHALL, W., R. J. BEAVER and B. M. BEAVER. Introduction to Probability and Statistics: Metric Version. 15th ed. Boston: Cengage, 2020, 744 pp. ISBN 978-0-357-11446-9. info
  • SPIEGELHALTER, D. J. The Art of Statistics: How to Learn from Data. 1st ed. New York: Basic Books, 2019, 448 pp. ISBN 978-1-5416-1851-0. info
  • ZÁHOROVÁ, V. Theory of Probability and Statistics: Study Material. Ed. 1st. Pardubice: University of Pardubice, 2014, 163 pp. ISBN 978-80-7395-804-6. info
  • LEVINE, D. M., D. STEPHAN and K. A. SZABAT. Statistics for Managers Using Microsoft Excel. 7th ed. Boston: Pearson, 2013, 757 pp. ISBN 0-13-306181-7. info
  • KLÍMEK, P. Applied Statistics for Economics. 1st ed. Bučovice: Martin Stříž,, 2010, 114 pp. ISBN 978-80-87106-32-7. info
  • RUSKEEPÄÄ, H. Mathematica Navigator: Mathematics, Statistics, and Graphics. 3rd ed. Boston: Elsevier/Academic Press, 2009, 1136 pp. ISBN 978-0-12-374164-6. info
  • ABELL, M. L., J. P. BRASELTON and A. RAFTER. Statistics with Mathematica. PAP/CDR ed. San Diego:: Academic Press, 1999, 632 pp. ISBN 978-0-12-041554-0. info
    recommended literature
  • SALKIND, N. J. Statistics for People Who (Think They) Hate Statistics. 6th ed. Los Angeles: SAGE Publications, 2016. ISBN 978-1-5063-3383-0. info
  • ROSE, C. and MURRAY D. SMITH. Mathematical Statistics with Mathematica. 1st ed. New York: Springer, 2002, 481 pp. ISBN 0-387-95234-9. info
Assessment methods
Course credit: active participation in seminars + ongoing tests. Examination: written test (60 % of the overall grade) + oral examination (40% of the overall grade).
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
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