Gurantor department | Department of Applied Mathematics | Credits | 4 |

Subject guarantor | Ing. Martina Litschmannová, Ph.D. | Subject version guarantor | Ing. Martina Litschmannová, Ph.D. |

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

Year | 1 | Semester | summer |

Study language | Czech | ||

Year of introduction | 2016/2017 | Year of cancellation | 2020/2021 |

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

Instruction secured by | |||
---|---|---|---|

Login | Name | Tuitor | Teacher giving lectures |

KRA0220 | Ing. Jan Kracík, Ph.D. | ||

LIT40 | Ing. Martina Litschmannová, Ph.D. |

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

Form of study | Way of compl. | Extent |

Full-time | Credit and Examination | 3+3 |

Part-time | Credit and Examination | 10+10 |

The course is designed for graduates to gain an initial idea of the basic concepts and tasks that fall within the field of probability and statistics and were able to apply their knowledge in practice.

Lectures

Tutorials

This is an introductory course in statistics. The course will emphasize methods of applied statistics and data analysis. Theoretical considerations will be included to the extent that knowledge of theory is necessary for a sound understanding of methods and contributes to the development of data analysis skills and the ability to interpret results of statistical analysis. The objective of the course is to develop sufficient knowledge of statistical tools and procedures, understanding of the underlying theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into other areas of his or her own work.

BERTSEKAS, Dimitri P. a TSISIKLIS, John N. Introduction to probability. Second edition. Nashua, NH: Athena Scientific, [2008]. ISBN 978-1886529236.
JAMES, Gareth; WITTEN, Daniela; HASTIE, Trevor a TIBSHIRANI, Robert. An introduction to statistical learning: with applications in R. Second edition. Springer texts in statistics. New York: Springer, [2021]. ISBN 978-1071614174.

WHEELAN, Charles. Naked Statistics: Stripping the Dread from the Data. W. W. Norton & Company, 2014. ISBN 978-0393347777.

Presence form:
Discussions:
- 10 short tests during the semester per 2 points, 20 points overall (minimum required: 6 points)
- 4 homeworks per 5 points, 20 points overall (minimum required: 5 points for each task)
Exam:
- 10 short tests during the semester per 2 points, 20 points overall (minimum required: 6 points)
Combined form:
Discussions:
- 3 homeworks during the semester per 10 points, for a total maximum of 30 points (minimum required: 3 points for each homework)
- Test with a maximum of 10 points (minimum required: 1 point)
- Semester project, max 20 points (minimum required: 10 points)
Exam:
- written exam (practical part: max. 50 points, required minimum: 25 points, theoretical part: max. 10 points, required minimum: 2 points)
For successful completion of the Discussions is given credit. Students will receive credit if they meet the required
minimum of each of the sub-tasks and compensatory gain at least 20 points.
Students will pass the exam if they meet the the required minimum of each of the sub-tasks and compensatory gain (Discussions and Exam) at least 51 points.

Presence form: Active participation in at least 80% of discussions.

Subject has no prerequisities.

Subject has no co-requisities.

1) Introduction to Probability Theory
2) Discrete random variable
3) Selected distributions of discrete random variables
4) Continuous random variable
5) Selected distributions of continuous random variables
6) Limit Theorems
7) Random Vector
8) Introduction to statistics, exploratory analysis
9) The survey, random sampling and basic sample characteristics
10) Introduction to estimation theory
11) Introduction to hypothesis testing (principle)
12) Hypotheses testing - mean, probability, variance (one-sample and two-sample tests)
13) Analysis of variance (verification normality, ANOVA and Kruskal-Wallis test)

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

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

Credit | Credit | 40 (40) | 20 | |

Průběžné testy | Written test | 20 | 6 | |

Domácí úkoly | Other task type | 20 | 10 | |

Aktivní účast | Other task type | |||

Examination | Examination | 60 (60) | 27 | 3 |

Praktická část | Written examination | 50 | 25 | |

Teoretická část | Written examination | 10 | 2 |

Show history

Conditions for subject completion and attendance at the exercises within ISP:

Show history

Academic year | Programme | Branch/spec. | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2018/2019 | (N2658) Computational Sciences | (2612T078) Computational Sciences | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | ||||

2017/2018 | (N2658) Computational Sciences | (2612T078) Computational Sciences | P | Czech | Ostrava | 1 | Choice-compulsory | study plan | ||||

2016/2017 | (N2658) Computational Sciences | (2612T078) Computational Sciences | P | Czech | Ostrava | 1 | 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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