Statistical Analysis and Experiment (FSI-9SAE)

Academic year 2021/2022
Supervisor: doc. RNDr. Libor Žák, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech or English
Aims of the course unit:
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.
Learning outcomes and competences:
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.
Course contents:
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.
Teaching methods and criteria:
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.
Controlled participation in lessons:
Attendance at lectures is not compulsory, but is recommended.
Type of course unit:
    Lecture  10 × 2 hrs. optionally                  
Course curriculum:
    Lecture 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.
Literature - fundamental:
1. Montgomery, D. C. - Renger, G.: Probability and Statistics. New York : John Wiley & Sons, Inc., 1996.
2. Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York : John Wiley & Sons, Inc., 1994.
3. Dowdy, S. - Wearden, S.: Statistics for Research. New York : John Wiley & Sons, Inc., 1993.
Literature - recommended:
1. Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993.
2. Meloun, M. - Militký, J._: Statistické zpracování experimentálních dat. Praha : PLUS, 1994.
3. Lamoš, F. - Potocký, R.: Pravdepodobnosť a matematická štatistika. Bratislava : Alfa, 1989.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
D-MAT-P full-time study --- no specialisation -- DrEx 0 Recommended course 3 1 W
CEITEC-AMN-EN-P full-time study --- no specialisation -- DrEx 0 Recommended course 3 1 W
CEITEC-AMN-EN-K combined study --- no specialisation -- DrEx 0 Recommended course 3 1 W
CEITEC-AMN-CZ-K combined study --- no specialisation -- DrEx 0 Recommended course 3 1 W
CEITEC-AMN-EN-Z visiting student --- no specialisation -- DrEx 0 Recommended course 3 1 W
D-MAT-K combined study --- no specialisation -- DrEx 0 Recommended course 3 1 W
CEITEC-AMN-CZ-P full-time study --- no specialisation -- DrEx 0 Recommended course 3 1 W