Gurantor department | Department of Geological Engineering | Credits | 5 |

Subject guarantor | Doc. PaedDr. Vladimír Homola, Ph.D. | Subject version guarantor | Doc. PaedDr. Vladimír Homola, Ph.D. |

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

Year | 2 | Semester | winter |

Study language | Czech | ||

Year of introduction | 2017/2018 | Year of cancellation | 2018/2019 |

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

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

HOM50 | Doc. PaedDr. Vladimír Homola, Ph.D. |

Extent of instruction for forms of study | ||
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Form of study | Way of compl. | Extent |

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

Part-time | Credit and Examination | 6+6 |

The aim of the course is to acquaint student with advanced methods of statistical data analysis that are currently widely used, not only in science and research, but also in the application sphere. These methods include mainly correlation and regression analysis, statistical hypothesis testing, analysis of variance (ANOVA), multivariate statistical analysis, modelling and visualization of monitored variables in 2D and 3D view etc. The main emphasis is on understanding the nature of advanced statistical methods, their application and practical use. In this course, students will learn to rigorously carry out an exploratory and confirmatory data analysis, including verification of the preconditions for the subsequent application of statistical methods mentioned above. An important part of the course is the student's own work with experimental data in an appropriate statistical software, subsequent evaluation and interpretation of the results.

Lectures

Tutorials

The course deals with the application of advanced methods of statistical data analysis and subsequent interpretations of obtained results. It explains the mathematical principles of various statistical methods and defines the basic conditions for their use. An emphasis is placed on the correct choice of methods in order to obtain the greatest amount of relevant information and knowledge for subsequent use in practice. An integral part of the course is also a practical application of methods to real data in software environment for statistical computing (according to the current state of scientific field, for example - Statgraphics, SPSS, SAS, Surfer etc.) during exercises in the computer lab.

MELOUN, Milan a Jiří MILITKÝ. Statistical data analysis: A practical guide with 1250 exercises and answer key on CD. Philadelphia: Woodhead Publishing India Pvt Ltd, 2011. ISBN 978-93-80308-11-1.
RENCHER, Alvin C. a William F. CHRISTENSEN. Methods of multivariate analysis. 3rd ed. Hoboken: Wiley, 2012. Wiley series in probability and statistics. ISBN 978-0-470-17896-6.

BERTHOUEX, P. Mac a Linfield C. BROWN. Statistics for environmental engineers. 2nd ed. Boca Raton: Lewis Publishers, 2002. ISBN 15-667-0592-4.
DEVORE, Jay L. a Kenneth N. BERK. Modern mathematical statistics with applications. 2nd ed. London: Springer, 2012. Springer texts in statistics. ISBN 14-614-0390-1.
MCKILLUP, Steve a M. Darby DYAR. Geostatistics explained: an introductory guide for earth scientists. Cambridge: Cambridge University Press, 2010. ISBN 978-0-521-74656-4.

Control questions, elaboration and submission of seminary work.

Active participation (min. 70%) in seminars and successful completion of credit tests. The exam - oral with written preparation.

Subject has no prerequisities.

Subject has no co-requisities.

1. Introduction to statistical data analysis
2. The issue writing of values (decimal places), rounding and missing values (limit of detection)
3. Exploratory data analysis – graphical techniques, outliers, statistical tests and data transformation
4. Confirmatory data analysis – classical and robust estimators of central tendency and variability, small datasets
5. Statistical hypothesis testing – parametric and nonparametric hypothesis tests
6. Linear regression and correlation analysis
7. Analysis of variance (ANOVA) – one-way ANOVA
8. Analysis of variance (ANOVA) – two-way ANOVA and MANOVA
9. Multivariate statistical methods – basic assumptions
10. Cluster analysis
11. Principal component analysis
12. Factor analysis
13. Two-dimensional interpolation, extrapolation and approximation
14. Three-dimensional Interpolation, extrapolation, approximation

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

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

Credit | Credit | 33 | 17 |

Examination | Examination | 67 | 18 |

Show history

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

2017/2018 | (N2102) Mineral Raw Materials | (3904T029) Mineral Biotechnology | P | Czech | Ostrava | 2 | Choice-compulsory | study plan | ||||

2017/2018 | (N2102) Mineral Raw Materials | (2102T006) Water Technologies and Water Management | K | Czech | Ostrava | 2 | Choice-compulsory | study plan | ||||

2017/2018 | (N2102) Mineral Raw Materials | (2102T006) Water Technologies and Water Management | K | Czech | Most | 2 | Choice-compulsory | study plan | ||||

2017/2018 | (N2102) Mineral Raw Materials | (2102T006) Water Technologies and Water Management | P | Czech | Ostrava | 2 | Choice-compulsory | study plan |

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