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ECTS Course Overview

Special Methods of Data Analysis

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code470-8542/02
Number of ECTS Credits Allocated5 ECTS credits
Type of Course Unit *Choice-compulsory
Level of Course Unit *First Cycle, Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech, English
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
BRI10prof. Ing. Radim Briš, CSc.
LIT40Ing. Martina Litschmannová, Ph.D.
Computer-based data processing requires their users to be able to analyze complex problems. This subject is a combination of lectures and computer-based practical, whereby theory is firmly placed into practice. In contrast to classical mathematical statistics, emphasis is placed not on particular methods but on their appropriate combinations, enabling the assessment of data quality, the selection of a suitable statistical model, its verification and the interpretation of the results with respect to the goal of data analysis. The learning is centered around focusing more on conceptual understanding of key concepts, and statistical thinking, and less on formulas and calculations, which can now be left to PCs. Statistical skills enable students to intelligently collect, analyze and interpret data relevant to their decision-making.
Learning Outcomes of the Course Unit
This subject could be considered a multidisciplinary subject in between statistics and informatics. Its aim is to expand basic knowledge of statistical methods acquired by students within the scope of the subject 541-0181 / 01 - Statistics and / or 548-0093 / 01 - Quantitative methods in geography, especially about advanced statistical methods used in technical practice combined with special computer-based procedures.

After passing the subject students should be able to effectively evaluate their own data and choose a suitable method for the creating a data model. They should be able to verify usability of the data model and they should be know how to interpret results in connection with the practical focus of the task.
Course Contents
Introduction to probability theory
Random Variable
Random Vector
Probability models for discrete random variable
Probability models for continous random variable
Statistical survey and exploratory analysis
Sample characteristics, Introduction to estimation theory
Hypothesis testing – principle
One-sample and two-samples parametric tests of hypothesis
Goodness of Fit tests
Tests for comparing more than two variances, ANOVA (one factor, two factors), Kruskal-Wallis test
Analysis of Independence
Introduction to Regression Analysis
Recommended or Required Reading
Required Reading:
Briš R., Probability and Statistics for Engineers, 2011, electronics script, Project CZ.1.07/2.2.00/15.0132. Dostupné z
Dummer R.M.; Introduction to Statistical Science, script of VŠB-TUO FEI, 1998, ISBN 80-7078-497-0
Briš R., Litschmannová M.,STATISTIKA I. pro kombinované a distanční studium, Elektronické skriptum VŠB TU Ostrava,2004.
Briš, R., Litschmannová M.: STATISTIKA II., 2007 Ostrava:VŠB – Technická univerzita Ostrava, 2007, ISBN 978-80-248-1482-7, elektronické skriptum.
Recommended Reading:
Briš R., Probability and Statistics for Engineers, 2011, electronics script, Project CZ.1.07/2.2.00/15.0132. Dostupné z
James.L.Johnson; Probability and Statistics for Computer Science, Wiley 2003, ISBN 0-471-32672-0
Likeš J., Cyhelský L.Hindls R.; Úvod do statistiky a pravděpodobnosti, 1994, VŠE Praha, ISBN 80-7079-028-8.
Likeš J., Machek J., Počet pravděpodobnosti, SNTL Praha 1981.
Likeš J., Machek J., Matematická statistika, SNTL Praha 1983.
Planned learning activities and teaching methods
Lectures, Project work
Assesment methods and criteria
Tasks are not Defined