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Woman on The Labour Market in the Situation of the Mother
Matějková, Zdenka ; Bartošová, Jitka (advisor) ; Bína, Vladislav (referee)
The target of this diploma thesis is to analyze the situation of Czech mother on the labour market. The theoretical part will be created from the accessible literary resources and public databases. By providing databases made into tables and charts of the most problematical influences which affect the situation of a woman on the Czech labour market. The practical part presents methodology of the diploma thesis. Then the typology of family policy in the chosen European countries is tested by means of cluster analysis. This section represents very important research part because the national family policy acts upon womans possibilities how to bring family and career into balance after finishing maternity and parental leave. Subsequently the questionairre Woman in the situation of mother on the labour market is spread into all regions of the Czech Republic and afterwards evaluated. On top of that that five hypothesis with different factors as a womans current income, education level and number of her children will be tested to deepen the issue of women chance on the Czech labour market.
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The use of cluster analysis to evaluation half-year measurement campaign of gaseous elemental mercury from atmospheric station Křešín u Pacova
Veselik, P. ; Dvorská, Alice
This paper deals with evaluation of gaseous elemental mercury in the air from Atmospheric Station Křešín u Pacova. The measurements were conducted with two identical instruments positioned right next to each other in one ground-based container in a 10 min time step, between December 2012 and June 2013. This measurement campaign was aggregated into approximately weekly intervals. The aim of this paper is to show the use of cluster analysis for finding those time periods which correspond the most according to appropriately selected criteria. Classification was performed by using cluster analysis of regression coefficients obtained by modelling weekly measurements by the second device on the measurements from the first device. Results of this analysis indicate the existence of five periods in which the regression lines show certain similarities. Further attention is paid to their analysis.
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Typology of EU member states' tax systems with an emphasis on Slovakia
Štefanský, Martin ; Kubátová, Květa (advisor) ; Kostohryz, Jiří (referee)
The main objective of this bachelor thesis is to create distinctive groups of member states of the European union in terms of their tax systems based on cluster analysis computed in the XLSTAT program on data from the annual report of the European Statistical Office (Eurostat). Partial aim´s task is to assign certain characteristics that best describe such defined groups of states. The theoretical part deals with the definition of the terms tax system, tax mix and tax quota, along with the factors that affect or distort these indicators. The practical part contains the general characteristics of the method of cluster analysis, with an emphasis on the k-means clustering method, which was applied in the analysis. The last chapter contains an author´s description of created clusters that are the outcome of our analysis in the XLSTAT program.
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Czech Republic and the adoption of euro on the background of macroeconomic imbalances
Caletka, Petr ; Bič, Josef (advisor) ; Žamberský, Pavel (referee)
This work is aimed to determine whether the Czech Republic is ready to enter to the third stage of European economic and monetary union which is associated with the adoption of the euro, and that regarding the fulfillment of the formal entry criteria and also in terms of alignment of the Czech economy with the rest of the eurozone. On that basis evaluate whether it is advantageous for the country to adopt the euro. The first part introduces the different stages of regional integration, as well as the theory of optimum currency areas and economic governance in the European Union. The second chapter is devoted to evaluate the readiness of the Czech Republic to join the euro zone from three perspectives. First, fulfillment of nominal convergence criteria is evaluated. Real convergence and macroeconomic imbalances within the euro area are assessed using cluster analysis. The second approach is to analyze whether EMU constitute an optimal currency area. At the end the experience, of three countries of the eastern enlargement, with changeover to a common currency are presented.
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Analysis of the similarity of the human development index values between European states
Šafaříková, Kristýna ; Malá, Ivana (advisor) ; Šulc, Zdeněk (referee)
Main goal of this thesis is to analyze human development index for European countries and provide cluster analysis not only of human development index but even of another quality of life variables and to find similarities between particular countries by using hierarchical methods. The first part focuses on quality of life and definition of human development index. Human development index is one possibility how to measure quality of life, there are mentioned another possibilities, though how to analyze it. The second part of the thesis focuses on cluster analysis definition, which is used for searching for similarities between particular countries. Five hierarchical cluster methods is used for classify countries into clusters. Euclidean metric is used for express the distance between countries. Similar variables between countries is judged according to sorting into clusters by hierarchical methods. Diploma thesis enlightens similarity between European countries from quality of life overview and provides statistical evidence about this topic. Results of the thesis confirms similarities between geographical close states.
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The Analysis of Security and Anonymity in Cryptocurrency Payments
Maňák, Michal ; Šebesta, Michal (advisor) ; Bruckner, Tomáš (referee)
Bitcoin and other cryptocurrencies are getting into common usage more and more frequently. Unfortunately this spread is connected with attempts to misuse their principles for personal benefit. The goal of this thesis is to explain the main principles of using bitcoin in relation to the internet identity. This connection is represented by two sources in the thesis: the first is a bitcoin blockchain, the second is the sum of various user profiles used in discussion forums and similar websites. The aim is to create and explain the process how to, by using the combination of this two sources, reveal the identity of a human using cryptocurrencies for payment (from a private person point of view). First, methods used for extraction of correct and useful data from the blockchain and internet profiles must be developed to be able to mine data from the joint database, merging into a full user profile containing personal data. Results of this investigation are extended by the rules of preserving anonymity on the internet, containing both technological and behavioural measures avoiding a share of personal data. The end of this thesis explains possible attack vectors that might be used for compromising cryptocurrencies. The methods of searching of the internet identity are a useful tool for facing these attacks.
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Discriminant and cluster analysis as a tool for classification of objects
Rynešová, Pavlína ; Löster, Tomáš (advisor) ; Řezanková, Hana (referee)
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can be a disordered group of objects organized into several internally homogeneous classes or clusters. Discriminant analysis creates knowledge based on the jurisdiction of existing classes classification rule, which can be then used for classifying units with an unknown group membership. The aim of this thesis is a comparison of discriminant analysis and different methods of cluster analysis. To reflect the distances between objects within each cluster, squeared Euclidean and Mahalanobis distances are used. In total, there are 28 datasets analyzed in this thesis. In case of leaving correlated variables in the set and applying squared Euclidean distance, Ward´s method classified objects into clusters the most successfully (42,0 %). After changing metrics on the Mahalanobis distance, the most successful method has become the furthest neighbor method (37,5 %). After removing highly correlated variables and applying methods with Euclidean metric, Ward´s method was again the most successful in classification of objects (42,0%). From the result implies that cluster analysis is more precise when excluding correlated variables than when leaving them in a dataset. The average result of discriminant analysis for data with correlated variables and also without correlated variables is 88,7 %.
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Some Robust Estimation Tools for Multivariate Models
Kalina, Jan
Standard procedures of multivariate statistics and data mining for the analysis of multivariate data are known to be vulnerable to the presence of outlying and/or highly influential observations. This paper has the aim to propose and investigate specific approaches for two situations. First, we consider clustering of categorical data. While attention has been paid to sensitivity of standard statistical and data mining methods for categorical data only recently, we aim at modifying standard distance measures between clusters of such data. This allows us to propose a hierarchical agglomerative cluster analysis for two-way contingency tables with a large number of categories, based on a regularized measure of distance between two contingency tables. Such proposal improves the robustness to the presence of measurement errors for categorical data. As a second problem, we investigate the nonlinear version of the least weighted squares regression for data with a continuous response. Our aim is to propose an efficient algorithm for the least weighted squares estimator, which is formulated in a general way applicable to both linear and nonlinear regression. Our numerical study reveals the computational aspects of the algorithm and brings arguments in favor of its credibility.
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Segmentation of Infant Milks Market
Čevela, Josef ; Koudelka, Jan (advisor) ; Sobol, Robert (referee)
The principal goal of the master thesis is to explore the similarities and possible differences among customers of infant milks. Based on those findings, the customers are clustered into various segments, which are extremely homogenous within each one, but significantly heterogeneous outside each of them. The second goal of the thesis is to suggest the best marketing solutions for each revealed segment. The work is divided into two parts, theoretical and practical. The theoretical part describes the process of market segmentation. The characteristics of the infant milk market and the description of infant milk types are described in the practical part. The customers' segmentation is designed according to primary and secondary research. Secondary data is based on data from project MML-TGI and content analysis. The data from my own primary research are processed by IBM SPSS statistics version 20. Among the main finding of the thesis was defining 5 segments of the infant milk customers. The work characterizes all of them and special term is chosen for each of the segment. Finally, the relevant marketing solutions are suggested for each of the segment.
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