Gurantor department | Department of Mathematics and Descriptive Geometry | Credits | 3 |

Subject guarantor | Mgr. Marcela Rabasová, Ph.D. | Subject version guarantor | Mgr. Marcela Rabasová, Ph.D. |

Study level | undergraduate or graduate | Requirement | Choice-compulsory |

Year | 1 | Semester | winter |

Study language | Czech | ||

Year of introduction | 2013/2014 | Year of cancellation | |

Intended for the faculties | USP | Intended for study types | Follow-up Master, Master |

Instruction secured by | |||
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Login | Name | Tuitor | Teacher giving lectures |

KAH14 | Mgr. Marcela Rabasová, Ph.D. |

Extent of instruction for forms of study | ||
---|---|---|

Form of study | Way of compl. | Extent |

Full-time | Credit and Examination | 2+2 |

Combined | Credit and Examination | 6+6 |

The aim of the course is to provide theoretical and practical foundation for understanding the importance of basic probability concepts and teach the student statistical thinking as a way of understanding the processes and events around us, to acquaint him with the basic methods of gathering and analyzing statistical data, 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 from the combinatorics and probability theory;
• formulate questions that can be answered by the data and understand principles of collecting, processing and presentation of the data;
• select and use appropriate statistical methods for data analysis;
• propose and evaluate conclusions (inference) and make predictions using the data.
The graduate of this course should be able:
• understand and use basic notions in combinatorics and probability theory
• formulate questions, which can be answered based on the given data, for this purpose learn the principles of collecting, processing data and presentation of relevant values and results
• choose and use suitable statistical methods for data analysis
• suggest and evaluate conclusions (inference) and predictions obtained from data

Lectures

Individual consultations

Tutorials

Other activities

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.

Dummer R. M.: INTRODUCTION TO STATISTICAL SCIENCE. VŠB-TU Ostrava 1998; ISBN 80-7078-497-0

Dummer R. M.: INTRODUCTION TO STATISTICAL SCIENCE. VŠB-TU Ostrava 1998; ISBN 80-7078-497-0

Two tests, 1 project - data analysis.

http://stattrek.com/tutorials/statistics-tutorial.aspx

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Subject has no prerequisities.

Subject has no co-requisities.

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

Task name | Type of task | Max. number of points
(act. for subtasks) | Min. number of points |
---|---|---|---|

Exercises evaluation and Examination | Credit and Examination | 100 (100) | 51 |

Exercises evaluation | Credit | 30 | 10 |

Examination | Examination | 70 | 21 |

Show history

Academic year | Programme | Field of study | Spec. | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|

2018/2019 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | |||

2017/2018 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | |||

2016/2017 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | |||

2015/2016 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | |||

2014/2015 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | |||

2014/2015 | (N3942) Nanotechnology | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | ||||

2013/2014 | (N3942) Nanotechnology | (3942T001) Nanotechnology | P | Czech | Ostrava | Choice-compulsory | study plan |

Block name | Academic year | Form of study | Study language | Year | W | S | Type of block | Block owner |
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