Gurantor department | Department of Environmental Engineering | Credits | 6 |

Subject guarantor | Mgr. Oldřich Motyka, Ph.D. | Subject version guarantor | Mgr. Oldřich Motyka, Ph.D. |

Study level | undergraduate or graduate | Requirement | Compulsory |

Year | 1 | Semester | winter |

Study language | English | ||

Year of introduction | 2022/2023 | Year of cancellation | |

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

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

MOT127 | Mgr. Oldřich Motyka, 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 | 3+3 |

Part-time | Credit and Examination | 12+12 |

Students will learn to analyse and assess quantitative, semi-quantitative and qualitative data. They will be capable of describing and visualising the data using the descriptive statistics, identifying and assessing the data outliers and the distribution of the data. They will also be able to formulate the statistical hypothesis, to use appropriate statistical test and to correctly interpret the results. Moreover, they will gain proficiency in correlation and regression data analysis, as well as in multivariate and spatial analysis.

Lectures

Tutorials

Students will be accustomed to preparation of the sampling plan, collection, modification and assessment of the data. They will know to appropriately use and interpret statistical methods based on the specifics of the particular dataset. The data assessment will be performed in the R environment and the respective libraries.

RUMSEY, Deborah, J. 2016, Statistics For Dummies, 2nd edition. Hoboken, NJ, USA: Wiley. ISBN 978-1-119-29352-1
BRUNSDON, Chris a Lex COMBER, 2019. An introduction to R for spatial analysis and mapping. Second edition. Los Angeles: SAGE. Spatial analytics and GIS (Sage). ISBN 978-152-6428-509
HOTHORN, Torsten a Brian EVERITT, c2014. A handbook of statistical analyses using R. 3rd ed. Boca Raton: CRC Press. Spatial analytics and GIS (Sage). ISBN 978-148-2204-582
HUSSON, Francois, Sebastian LE a Jérôme PAGÈS, 2017. Exploratory Multivariate Analysis by Example Using R. 2nd ed. Boca Raton: Chapman and Hall/CRC. ISBN 9780-429-225-437

VENABLES, William M. & SMITH, David M., 2009. An Introduction to R. 2nd edition, Network Theory Ltd. ISBN 978-0954612085
MAINDONALD, J. H. a John BRAUN, 2010. Data analysis and graphics using R: an example-based approach. Third edition. Cambridge: Cambridge University Press. Cambridge series on statistical and probabilistic mathematics. ISBN 978-113-9194-648
WICKHAM, Hadley, 2016. Ggplot2: elegant graphics for data analysis. Second edition. [Cham]: Springer. Use R!. ISBN 978-3-319-24277-4
ZELTERMAN, Daniel, 2015. Applied multivariate statistics with R. Cham: Springer. Statistics for biology and health (Springer). ISBN 978-3-319-14093-3

Students provide their own dataset, discussion of its analysis is a part of the exam.
The exam consists of both written and oral part.

Active participation in seminars.

Subject has no prerequisities.

Subject has no co-requisities.

1. Sampling plan preparation, management and saving of the data
2. Types of data - quantitative and qualitative, description, characteristcs of variability, visualisation, outlier identification.
3. Hypothesis testing - null and alternative hypothesis, type I. and II. errors, statistical test and its power, p-value.
4. Introduction to the R environment and R studio interface, projects creation, data import, graphic outputs.
5. One-sample and two-sample tests - parametric and non-parametric methods, categorical data analysis - chi-squared and Fisher test.
6. Analysis of variance (ANOVA) - assessment of variances and normality, Kruskal-Wallis test - non-parametric alternative to ANOVA.
7. Correlation analysis - Pearson and Spearman correlation coefficent, data similarity measures (coefficients of similarity, correlation, covariance).
8. Regression analysis - linear regression, linear model assumptions, regression model parametres, coefficient of determination, statistical tests.
9. Regression analysis - polynomial regression, statistical tests, residual analyses.
10. Multiple linear regression - types of variable interactions, multicolinearity, missing data problems, applications.
11. Spatial data, autocorrelation, sampling, analysis, local and global statistics.
12. Multivariate analysis of data - principles, assumptions and data modification prior to the analysis.
13. Exploratory analysis, Principle component analysis (PCA), Multiple correspondence analysis (MCA), Factorial analysis of mixed data (FAMD), cluster analysis.

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 | |||

Examination | Examination | 100 | 51 | 3 |

Show history

Conditions for subject completion and attendance at the exercises within ISP: - seminar work, topic is going to be assigned following consultation with the student - proving ability to analyze data on the example of data (with a commentary) produced by a student - in-person exam - written and oral part

Show history

Academic year | Programme | Branch/spec. | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
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2024/2025 | (N0532A330044) Geoinformatics | P | English | Ostrava | 1 | Compulsory | study plan | |||||

2023/2024 | (N0532A330044) Geoinformatics | P | English | Ostrava | 1 | Compulsory | study plan |

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