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 | Compulsory |

Year | 3 | Semester | winter |

Study language | Czech | ||

Year of introduction | 2019/2020 | Year of cancellation | |

Intended for the faculties | FEI | Intended for study types | Bachelor |

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

Login | Name | Tuitor | Teacher giving lectures |

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

VRT0020 | Mgr. Adéla Vrtková |

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

Form of study | Way of compl. | Extent |

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

Part-time | Credit and Examination | 8+8 |

This subject is an introductory course of statistics. The aim 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.

Lectures

Tutorials

Project work

Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. This course will teach students the basic concepts used to describe data. With the knowledge gained in this course, students will be ready to undertake their first very own data analysis using the open source software R, which is rapidly becoming the leading programming language in statistics and data science.

[1] CRAWLEY, Michael J. Statistics: an introduction using R. Chichester, West Sussex, England: J. Wiley, c2005. ISBN 978-0470022986
[2] StatSoft, Inc. (2013). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com

[1] Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

Discussions:
A student may earn a maximum of 40 points for graded assignments during the semester. The minimum number of points for credit is 20.
Full-time study:
In the first part of the semester, each student will complete three homework assignments (1P - 3P) focused on probability theory (max. 20 points, min. 6 points).
In the second part of the semester, students will complete four homework assignments (1S - 4S) focused on descriptive statistics. Each of the assignments will be assessed with a maximum of 5 points. To be awarded credit, a minimum of 10 points must be obtained from these assignments, i.e. max. 20 points, min. 10 points.
You will be informed about the method of assignment and the deadline for submission of each assignment in LMS Moodle.
The results will be recorded in Edison in aggregate, always after marking the relevant set of homework (1P - 3P and 1S - 4S).
Combined study:
Students will complete 4 homework assignments during the semester with a maximum of 10 points, for a total maximum of 40 points (required minimum: 3 points per homework assignment).
Exam:
- Written exam (max. 60 points, required minimum: 30 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.

Subject has no prerequisities.

Subject has no co-requisities.

1) Introduction to Probability Theory
2) Conditonal probability, Bayes Theorem
3) Discrete random variable
4) Discrete probability distributions
5) Continuous random variable
6) Continous probability distributions
7) Random Vector
8) Exploratory data analysis - qualitative variable and two qualitative variables
9) Exploratory data analysis - quantitative variable
10) Exploratory data analysis - two quantitative variables (independent variables vs. paired data)
11) Introduction to statistical induction, Introduction to estimation theory
12) Introduction to hypothesis testing (principle, hypothesis testing, statistical vs. practical significance, p-value)
13) One sample tests of mean and binomial test of proportion

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 | |

Homeworks | Other task type | 20 | 6 | |

Homeworks | Other task type | 20 | 10 | |

Examination | Examination | 60 | 30 | 3 |

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Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines.

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

2023/2024 | (B0714A060016) Biomedical Technology | P | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2023/2024 | (B0714A060016) Biomedical Technology | K | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2022/2023 | (B0714A060016) Biomedical Technology | K | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2022/2023 | (B0714A060016) Biomedical Technology | P | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2021/2022 | (B0714A060016) Biomedical Technology | K | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2021/2022 | (B0714A060016) Biomedical Technology | P | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2020/2021 | (B0714A060016) Biomedical Technology | K | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2020/2021 | (B0714A060016) Biomedical Technology | P | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2019/2020 | (B0714A060016) Biomedical Technology | P | Czech | Ostrava | 3 | Compulsory | study plan | |||||

2019/2020 | (B0714A060016) Biomedical Technology | K | Czech | Ostrava | 3 | Compulsory | study plan |

Block name | Academic year | Form of study | Study language | Year | W | S | Type of block | Block owner |
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2022/2023 Winter |

2021/2022 Winter |