Course detail

Statistical Analysis

FSI-9STAAcad. year: 2023/2024

The course is intended for the students of doctoral degree programme and it is concerned with the modern methods of statistical analysis (random sample and its realization, distribution fitting and parameter estimation, statistical hypotheses testing, regression analysis) for statistical data processing gained at realization and evaluation of experiments in terms of students research work.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

Rudiments of the probability theory and mathematical statistics.

Rules for evaluation and completion of the course

The exam is in form read report from choice area of statistical methods or else elaboration of written work specialized on solving of concrete problems.
Attendance at lectures is not compulsory, but is recommended.


The objective of the course is formalization of stochastic thinking of students and their familiarization with modern methods of mathematical statistics and possibilities usage of professional statistical software in research.
Students acquire higher knowledge concerning methods of mathematical statistics, which enable them to apply stochastic models of technical phenomena and processes by means calculations on PC.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Montgomery, D. C. - Renger, G.: Probability and Statistics. New York : John Wiley & Sons, Inc. 2010. (EN)
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004. (EN)
Anděl, J.: Základy matematické statistiky. Praha: Matfyzpress, 2011. (CS)
Meloun, M., Militký, J.: Kompendium statistického zpracování dat. Praha: Academia 2002. (CS)

Recommended reading

Anděl, J.: Statistické metody. Praha : Matfyzpress, 2007. (CS)
Meloun, M. - Militký, J._: Statistické zpracování experimentálních dat. Praha : PLUS, 1994. (CS)


Classification of course in study plans

  • Programme D-ENE-K Doctoral, 1. year of study, winter semester, recommended
  • Programme D-ENE-P Doctoral, 1. year of study, winter semester, recommended

Type of course unit



20 hours, optionally

Teacher / Lecturer


Probability distributions for modeling of technical phenomena and processes.
Exploratory analysis for statistical data processing.
Random sample - model and properties.
Search methods of probability distributions.
Estimation of probability distributions parameters.
Testing statistical hypotheses of distributions.
Testing statistical hypotheses of parameters.
Introduction to ANOVA, nonparametric tests.
Elements of linear regression analysis.
Statistical software - properties and option use.