The study is concerned with effective tax rates, namely implicit tax rates in the European Union over the period 2000-2012. The theoretical part includes tax incidence, description of individual implicit tax rates and their structure, and Eurostat metodology for measuring the impact of taxation on economic functions. The practical part in mainly dedicated to the development of implicit tax rates in the European Union and then to the evaluation of development trends in the implicit rates on consumption, labour and capital. The analysis is processed in the computer program STATISTICA. The classify is made by cluster analysis that should find the similar implicit tax rates in 2000, 2008 and 2012. Subsequently, the trends ort he development of individual implicit tax rates in EU countries are evaluated.

The objective of the thesis is to investigate the development of excise taxes indicators in twenty-seven member states of the European Union (without Croatia due to the absence of data). The selected period for the analysis is the year 2004 - 2016. The main aim of the thesis is to define the groups of member states EU according to their importance and similarity with respect to the excise duties. With this aim is related the another important idea of the work. That is whether the indicators of excise duties are brought closer to the harmonization processes and if are the member states converging. The cluster analysis was used to achieve the main goal of the work. The data which is used in this analysis is obtained from Eurostat and European Commission databases. The results of cluster analyzes allow the member states to be divided into four of the most stable groups. The group of Nordic countries in Europe, group of border states, states of NMS-12 able to approach the EU-15 and other states of NMS-12. The cluster analyzes also shows that the original and new member states, that joined the EU in 2004 and later, are still different in the field of excise duties. Differences that exist despite the strict harmonization of individual tax systems, in particular the harmonization of excise rates, may be caused, in particular, to the historical roots of individual countries, but also by the economic and political situation.

This dissertation is based on the perspective of the new theory of optimum currency areas (OCA) and the coherence of business cycles is considered as a prerequisite for creating monetary union. The OCA theory is applied both to the EU countries and US states and census regions. Measuring the coherence of business cycles was performed using a composite indicator, which consists of business cycles synchronicity and similarity indices. Cluster analysis was performed to identify groups of countries with similar characteristics of business cycles.

In five thematic sections was realized the cluster analysis which is one of more-dimensional statistical methods. In the base of monitored indexes in thematic sections were identified the general, significant attributes and differences chosen regions. In the base of results of quantitative analyses was made the SWOT analysis and elaborated the recommendations to increase their attractivity.

This thesis deals with the relation between marriage and parenthood in the Czech Republic. The main object is to explore changes in the family behavior of young generations and to analyze the related demographic processes of fertility and nuptality. Special attention is paid to marital, extramarital and premarital fertility. The thesis searches for the answer of the question if the marital surroundings are still unique for the child upbringing or if the economic and value changes of the society after 1989 broke the traditional family formation. Another important object is to perform a cluster analysis. Czech districts are divided into the clusters, which are made to describe the current situation. The last part demonstrates the results of the quantitative survey, which helps to find out the public attitudes about family life.

General characteristic of web mining including methodology and procedures incorporated into this term. Relation to other areas (data mining, artificial intelligence, statistics, databases, internet technologies, management etc.) Web usage mining - data sources, data pre-processing, characterization of analytical methods and tools, interpretation of outputs (results), and possible areas of usage including examples. Suggestion of solution method, realization and a concrete example's outputs interpretation while using above mentioned methods of web usage mining.

S rostoucím množstvím shromažďovaných a ukládaných dat vzniká potřeba shlukovacích metod, které by se dokázaly vypořádat i s rozsáhlými datovými soubory. Proto se objevuje množství nových algoritmů, vycházejících jak ze statistických přístupů, tak i z oblasti strojového učení. Cílem této diplomové práce je stručně představit dostupné metody shlukové analýzy a zhodnotit jejich silné a slabé stránky při analýze velkých souborů. Obsahem teoretické části je shrnutí základních pojmů a principů, které jsou všem metodám společné, a popisu nejznámějších metod shlukové analýzy. Ten obsahuje stručné vysvětlení, na jakém principu fungují a jaké výhody nebo případné nedostatky můžeme při jejich použití očekávat. Praktická část práce je věnována vlastnímu testování osmi metod dostupných v komerčním (SPSS, S-PLUS, STATISTICA) nebo akademickém (Weka) softwaru. Pro testování jsou použity umělé soubory se specifickými charakteristikami, které jsem vygeneroval pomocí vlastního algoritmu. Ten je rozšířením Neyman-Scottova procesu a kromě sférických shluků generuje i shluky nepravidelných tvarů. Výsledky potvrzují očekávání vycházející z teoretických předpokladů. Přinášejí však možnost kvantifikace vlivu charakteru dat na vhodnost jednotlivých metod.

My diploma thesis focuses on the comparison of possibilities of the statistical software SAS and SPSS in the area of the factor and cluster analysis and the multidimensional scaling. They deal with the methods for identifying groups of the similar statistical values (variables). The ascertained relations among the variables can serve to decrease the proportion vectors of the variables, which describe the individual monitored objects (statistical units), which helps us to apply other various methods, for example the regression or discriminant analysis. By one of the ways for finding the similarity of variables in the cluster analysis or the multidimensional scaling is searching for their relations. Whereas the base of the factor analysis is the formulation of the relation between two variables by means of the covariances, eventually Pearson correlation coefficient, it is possible to use also coefficients of correlation for the cluster analysis and the multidimensional scaling, in some case other measures. The thesis describes mainly the command syntax of the procedures implemented in SAS and SPSS. The meaning of the individual parametres and the partial specifications of each command are explained. The results gained by various types of analyses are compared on the basis of the real dataset. The possibilities of the statistical software SAS and SPSS are evaluated in the conclusion and it is referred to their advantages or disadvantages. The attention is also paid, for example, to the form of the input dataset, to the quaility of outputs or to the partial methods.

This master thesis is divided into two parts. First part focuses on production and consumption of electricity in the Czech republic, describing sources of the production and their progress since 1990, considering also the main electricity consumers. The end of the first part focuses on the Box-Jenkins methodology, time series of electricity consumption between 1981 and 2008 are created as well as forecasted up to 2011. The second part of this thesis describes the cluster analysis. This is used for finding similarities of the 27 states of the European Union. Cluster analysis is applied for years 1990 and 2007, for both electric power production and electric power consumption.

The objective of this thesis is the evaluation of selected coefficients of the cluster analysis when determining the optimal number of clusters. The analytical evaluation is performed on 20 independent real datasets. The analysis is made in statistical SYSTAT 13.1 Software. The application of coefficients RMSSTD, CHF, PTS, DB and Dunn's index on real datasets is the main part of this thesis, because the issue of evaluating the results of clustering is not devoted sufficient attention in scientific publications. The main goal is whether the selected coefficients of clustering can be applied in the real situations. The second goal is to compare selected clustering methods and their corresponding metrics when determining the optimal number of clusters. In conclusion, it is found that the optimal number of clusters determined by the coefficients mentioned above cannot be considered to be correct since, after application to the real data, none of the selected coefficients overcome the success rate of 40%, hence, the use of these coefficients in practice is very limited. Based on the practical analysis, the best method in identifying the known number of clusters is the average linkage in connection with the Euclidean distance, while the worst is the Ward's method in connection with the Euclidean distance.