Course detail
Statistics
FP-BstatPAcad. year: 2017/2018
Random Events: Probability and their properties, conditioned probability, classical probability, independed events, total probability.
Random Variables: Random variables of discrete and continuous type, characteristics and distribution laws, distribution binomial, hypergeometric, geometric, Poisson, normal and exponential.
Mathematical Statistics: Processing univariate sample data with a quantitative variable, points and intervals estimation of population parameters, testing statistical hypothesis.
Index Numbers: Simple and composite index numbers, Laspeyres and Paasche index numbers.
Regression Analysis: Method of least-squares, regression line, special regression function.
Time Series: Characteristics of time series, decomposition of time series, trend in a time series.
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 theoretical questions,
- 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.
Processing data samples.
Tests of statistical hypotheses.
Composite and aggregate index numbers.
Regression analysis.
Time series.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
SEGER, J. aj. Statistické metody v tržním hospodářství. Praha : Victoria Publishing, 1995. ISBN 80-7187-058-7. (CS)
Recommended reading
SWOBODA, H. Moderní statistika. Praha : Svoboda, 1977. (CS)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Practice in the industrial business subject
3. Classical probability
4. Conditional probability
5. Discrete random variables
6. Continuous random variables
7. Processing of data files
8. Interval estimates
9. Tests of statistical hypotheses
10. Individual indexes
11. Aggregate indexes
12. Regression Analysis
13. Time series
Exercise
Teacher / Lecturer
Syllabus
2. Practice in the industrial business subject
3. Classical probability
4. Conditional probability
5. Discrete random variables
6. Continuous random variables
7. Processing of data files
8. Interval estimates
9. Tests of statistical hypotheses
10. Individual indexes
11. Aggregate indexes
12. Regression Analysis
13. Time series