Course detail

Statistical Analysis

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.

Prerequisites

Rudiments of the probability theory and mathematical statistics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

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

Czech, English

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.

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer

Syllabus

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.