541-0135/01 – Advanced Statistical Methods for Data Processing (PSM)
| 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 |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
Summary
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.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Control questions, elaboration and submission of seminary work.
E-learning
Other requirements
Active participation (min. 70%) in seminars and successful completion of credit tests. The exam - oral with written preparation.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.