Course detail

Mathematics II

FSI-9MA2Acad. year: 2020/2021

Graphic analysis. Stratification. Multi-vari analysis. Multidimensional regression analysis, ANOVA, Simple sorting, double sorting, interaction. Category analysis.

Language of instruction

Czech

Number of ECTS credits

0

Mode of study

Not applicable.

Guarantor

doc. RNDr. Libor Žák, Ph.D.

Department

Institute of Mathematics (ÚM)

Learning outcomes of the course unit

Students acquire needed knowledge from important parts of the probability theory and mathematical statistics, which will enable them to evaluate and develop stochastic models of technical phenomena and processes based on these methods.

Prerequisites

Rudiments of descriptive statistics, probability theory and mathematical statistics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through consultations to explanation of basic principles and theories of the discipline.

Assesment methods and criteria linked to learning outcomes

Use of the above-mentioned statistical methods for solving specific problems. Specific problems are selected in agreement with the student. Student's area of study is preferred. The solved, calculated and elaborated tasks serve to evaluate the student.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Familiarization of students with multidimensional data evaluation. Focused primarily on multidimensional regression analysis, ANOVA and categorical analysis with real-world applications in technical practice.

Specification of controlled education, way of implementation and compensation for absences

Teaching is a form of consultation.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

D. C. Montgomery: Design of experiments, John Wiley & Sons, NY 1991 (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme D-IME-P Doctoral, 1. year of study, winter semester, recommended

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer

doc. RNDr. Libor Žák, Ph.D.

Syllabus

1. Graphic analysis.
2. Stratification.
3. Multi-vari analysis.
4. ANOVA.
5. Fixed and random effects model.
6. One-way analysis.
7. Two-way analysis.
8. Interaction.
9. Tukey's method
10 Scheffe's method.