Course detail

Statistical Analysis and Experiment

FSI-9SAEAcad. year: 2021/2022

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

Czech

Mode of study

Not applicable.

Guarantor

doc. RNDr. Libor Žák, Ph.D.

Department

Institute of Mathematics (ÚM)

Learning outcomes of the course unit

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.

Prerequisites

Rudiments of the probability theory and mathematical statistics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline.

Assesment methods and criteria linked to learning outcomes

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.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is not compulsory, but is recommended.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Dowdy, S. - Wearden, S.: Statistics for Research. New York : John Wiley & Sons, Inc., 1993. (EN)
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York : John Wiley & Sons, Inc., 1994. (EN)
Montgomery, D. C. - Renger, G.: Probability and Statistics. New York : John Wiley & Sons, Inc., 1996. (EN)

Recommended reading

Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993. (CS)
Lamoš, F. - Potocký, R.: Pravdepodobnosť a matematická štatistika. Bratislava : Alfa, 1989. (CS)
Meloun, M. - Militký, J._: Statistické zpracování experimentálních dat. Praha : PLUS, 1994. (CS)

Classification of course in study plans

  • Programme D-MAT-P Doctoral 1 year of study, winter semester, recommended course
  • Programme D-MAT-K Doctoral 1 year of study, winter semester, recommended course
  • Programme CEITEC-AMN-CZ-P Doctoral 1 year of study, winter semester, recommended course
  • Programme CEITEC-AMN-EN-P Doctoral 1 year of study, winter semester, recommended course
  • Programme CEITEC-AMN-EN-K Doctoral 1 year of study, winter semester, recommended course
  • Programme CEITEC-AMN-CZ-K Doctoral 1 year of study, winter semester, recommended course
  • Programme CEITEC-AMN-EN-Z Doctoral 1 year of study, winter semester, recommended course

Type of course unit

 

Lecture

20 hod., optionally

Teacher / Lecturer

doc. RNDr. Libor Žák, Ph.D.

Syllabus

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