546-0182/02 – Methods of Data Processing and Analysis (MAD)
| 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 | Follow-up Master, Bachelor |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
Summary
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.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Students provide their own dataset, discussion of its analysis is a part of the exam.
The exam consists of both written and oral part.
E-learning
Other requirements
Active participation in seminars.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.