Course detail

Statistical Methods

FAST-HA51Acad. year: 2024/2025

- Programming in Matlab
- Basic notions of the theory of probability and mathematical statistics
- Propagation of errors
- Elipse and elipsoid of errors
- Variance analysis with one factor
- Variance analysis with two factors
- Testing of normality distribution
- Regression analysis
- Some notions of Kalman filtering

Language of instruction

Czech

Number of ECTS credits

2

Mode of study

Not applicable.

Guarantor

Ing. Pavel Špaček, Dr.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

Entry knowledge

Basics of the theory of functions of one and several real variables (derivative, partial derivative, limit, continuous functions, graphs of functions, integral). Basic operations with matrices and vectors. Notions randomn variable, basic properties in the theory of probability, notions in the theory of estimation, basics of the theory of testing.

Rules for evaluation and completion of the course

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Aims

Give the students a basic understanding of the theory of probability. Students should be able to interpret the basics of mathematical statistics.
Teach the students how to evaluate data focussing on finding solutions to "unsolvable" systems of linear equations with minimum errors.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

KOUTKOVÁ, H., MOLL, I.: Úvod do pravděpodobnosti a matematické statistiky. Akademické nakladatelství CERM, s.r.o., 2001. (CS)

Recommended literature

Huaan, Fan: Theory of Errors and Least Squares Adjustment. Royal Institute of Technology, Stockholm, Sweden, 2003. ISBN 91-7170-200-8. (EN)

Type of course unit

 

Exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Pavel Špaček, Dr.

Syllabus

- Basic notions of the theory of probability and mathematical statistics - Propagation of errors - Elipse and elipsoid of errors - Variance analysis with one factor - Variance analysis with two factors - Testing of normality distribution - Regression analysis - Some notions of Kalman filtering