Applications of Statistics and Data Processing
Code  Completion  Credits  Range  Language 

18AS  Z,ZK  2  1P+1C  Czech 
 Lecturer:
 Jana Sekničková (guarantor)
 Tutor:
 Jana Sekničková (guarantor)
 Supervisor:
 Department of Software Engineering
 Synopsis:

The lecture links to previous analogue courses with significant emphasis of relationship between mathematical models
and practical application and warrant of inevitability of this relationship
 Requirements:
 Syllabus of lectures:

1. Concept of statistical thinking, statistics as basic modern literacy and inevitability in research of real patterns and in
all areas of application.
2. Definitions of „probability“. Areas of their applications and use in modeling of problems in science, technics,
economics and elsewhere.
3. Conditional probabilities, statistical independence, correlation; their interpretation in practical use.
4. Bayes attitude as one of basic principles in evaluation of experimental material.
5. Statistical characteristic of population and statistical sample.
6. Punctual estimation in discreet and continuous case of random magnitude, through statistical characteristics.
7. Laws of big numbers as mathematical solutions for formulation of interval estimation.
8. Central limit theorem and its use in statistical research.
9. Testing of hypothesis as a specific way of thinking and evaluation of experimental material.
10. Regressive and correlational analysis as a specific way to find out connections, hidden in statistical experimental
material.
 Syllabus of tutorials:
 Study Objective:
 Study materials:

Key references:
[1] Mendenhall, W., Sincich, T.T.:A Second Course in Statistics: Regression Analysis, 8th edition, Pearson Education,
2019.
[2] Kinney, J.J.: Probability: An Introduction with Statistical Applications, John Kinney, Colorado Springs, 2015.
Recommended references:
[3] Bhattacharya, R., Waymire, E.C.: A Basic Course in Probability Theory, Springer, 2017.
[4] Spanos, A.: Probability Theory and Statistical Inference: Empirical Modeling with Observational Data, 2nd edition,
Cambridge University Press, 2019.
 Note:
 Timetable for winter semester 2021/2022:
 Timetable is not available yet
 Timetable for summer semester 2021/2022:
 Timetable is not available yet
 The course is a part of the following study plans: