FSI-9STAAcad. year: 2016/2017Winter semesterNot applicable.. year of study1 credit
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.
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.
Rudiments of the probability theory and mathematical statistics.
Recommended optional programme components
Recommended or required reading
Montgomery, D. C. - Renger, G.: Probability and Statistics. New York : John Wiley & Sons, Inc., 1996.
Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993.
Meloun, M. - Militký, J._: Statistické zpracování experimentálních dat. Praha : PLUS, 1994.
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York : John Wiley & Sons, Inc., 1994.
Lamoš, F. - Potocký, R.: Pravdepodobnosť a matematická štatistika. Bratislava : Alfa, 1989.
Dowdy, S. - Wearden, S.: Statistics for Research. New York : John Wiley & Sons, Inc., 1993.
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.
Language of instruction
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.
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.