Course detail
Statistics 1
FP-STA1DAcad. year: 2018/2019
Students will acquire basic knowledge of probability theory, random variables, random vector, system reliability, index analysis, decision making for risks and uncertainties, descriptive statistics.
Language of instruction
Number of ECTS credits
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
- participation in seminars,
- submitting answers to elaboration calculating projects.
EXAM: The exam has a written form.
In the first part of the exam student solves 4 examples within 80 minutes. (It is allowed to use recomended literature.)
In the second part of the exam student works out answers to 3 theoretical questions within 15 minutes.
The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in control tests, achieved to calculating projects elaboration;
- points achieved by answering theoretical questions.
The grades and corresponding points:
A (100-90), B (89-83), C (82-76), D (75-69), E (68-60), F (59-0).
Course curriculum
Discrete random variables.
Continuous random variables.
Random vector
Reliability of systems
Index analysis
Decision making analysis
Processing data samples.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
HINDLS, R. aj. Analýza dat v manažerském rozhodování. Praha : Grada Publishing, 1999. ISBN 80-7169-255-7. (CS)
KROPÁČ, J. Statistika. 2. vyd. Brno: Akademické nakladatelství CERM, 2012. ISBN 978-80-7204-788-8. (CS)
SWOBODA, H. Moderní statistika. Praha : Svoboda, 1977. (CS)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Discrete random variables.
Continuous random variables.
Random vector
Reliability of systems
Index analysis
Decision making analysis
Processing data samples.
Exercise
Teacher / Lecturer
Syllabus