Course detail
Statistical Analysis and Experiment
FSI-9SAEAcad. year: 2022/2023
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
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
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
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
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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.