714-0586/01 – Statistics (S)
Gurantor department | Department of Mathematics and Descriptive Geometry | Credits | 4 |
Subject guarantor | Mgr. Marcela Rabasová, Ph.D. | Subject version guarantor | Mgr. Marcela Rabasová, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 2 | Semester | summer |
| | Study language | Czech |
Year of introduction | 2002/2003 | Year of cancellation | 2019/2020 |
Intended for the faculties | HGF | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide theoretical and practical foundation for understanding of the meaning of basic probability terms and teach the student to statistical thinking as a way of understanding of the processes and events around us, to acquaint him with the basic methods of statistical data gathering and analyzing, and to show how to use these general procedures in other subjects of study and in practice.
Graduates of this course should be able to:
• understand and use the basic terms of combinatorics and probability theory;
• formulate questions that can be answered by the data, learn the principles of data collecting, processing and presenting;
• select and use appropriate statistical methods for data analysis;
• propose and evaluate conclusions (inferences) and predictions using the data.
Teaching methods
Lectures
Tutorials
Summary
Combinatorics and probability. Random events, operations with them, sample space.
Definitions of events' probability - classical, geometrical, statistics. Conditional probability. Total probability and independent events.
Random variable and its characteristics.
Basic types of probability distributions of discrete random variables.
Basic types of probability distributions of continuous random variables.
Random vector, probability distribution, numerical characteristics.
Statistical file with one factor. Grouped frequency distribution.
Statistical file with two factors.
Regression and correlation.
Random sample, point and interval estimations of parameters.
Hypothesis testing.
Compulsory literature:
Radim Briš, Petra Škňouřilová. STATISTICS I. VŠB - Technical University of Ostrava, Ostrava 2007.
Recommended literature:
Way of continuous check of knowledge in the course of semester
Conditions for passing the course
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Course credits
- attendance on tutorials is obligatory, 20% of absence can be apologized,
- obligatory completion of tests, each test can be repeated once (student can get 0-15 points from tests),
- submission of programs in required time and prescribed form (students will gain 5 points for programs).
Point classification: 5-20 points.
Exam
Practical part of an exam is classified by 0-60 points. Practical part is successful if student obtains at least 25 points.
Theoretical part of the exam is classified by 0-20 points. Theoretical part is successful if student obtains at least 5 points.
Student must successfully pass both parts of the exam in the same day.
Point quantification in the interval and National grading scheme
100 - 86 85 - 66 65 - 51 51 - 0
excellent very good satisfactory failed
E-learning
Other requirements
They are no other requirements for students.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Combinatorics
2. Introduction to probability
3. Conditional probability and independent events. Bayes' theorem. Theorem of total probability
4. Random variable and its characteristics
5.-7. The basic distributions of discrete and continuous random variable
8. Random vector
9. Statistical file with one factor
10. Statistical file with two factors
11. Regression and correlation
12. Point and interval estimates of parameters
13. Hypothesis testing
14. Reserve
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction