This diploma thesis deals with the satisfaction of PhD students with facilities for the study by means of data gained from DOKTORANDI 2014 survey. The aim of the thesis is to identify factors that influence the satisfaction with facilities for PhD studies and finding similarities among different fields of studies according to satisfaction with facilities. The first part of this thesis contains a description of higher education with a focus on PhD programs and a description of statistical methods that are subsequently used in analytical part and a description of DOKTORANDI 2014 survey. The analytical part aims to answer the questions which factors affect the PhD students´ satisfaction with facilities for study using logistic regression and decision trees. Further it tries to determine the satisfaction similarities of PhD study fields with facilities for studying using cluster analysis.

The topic of the present thesis is an analysis of a sample data archive containing measured values of real and reactive power. The measurement in question took place in late 2006 and early 2007 using MEg40 recording measurement devices disposed in a station for transforming high voltage to low voltage in the Pražská energetika distribution network. The procedure of processing measured values, the preparation thereof for a subsequent processing by a neural network, and a final statistical evaluation of determined individual clusters -- typical daily take-off diagrams -- will be described. The results of the present thesis may be applied in the making of predictions of electrical energy consumption at a particular transformer station.

Cluster analysis is one of the classification methods of multivariate statistical analysis. The task of this analysis is to classify the objects into clusters so that objects inside these clusters are as similar as possible. The aim of this study is to evaluate the success of the classification of objects using six hierarchical cluster analysis methods. To reflect the distance between the objects, are used squared Euclidean and Mahalanobis distances. The success methods are evaluated through the information, which cluster the object belongs to, and this information is already contained in the data files. This thesis pointed out that the Ward's method is one of the most successful hierarchical method in a classification of objects into clusters. This method has been more successful in sorting objects than the other hierarchical methods, both in the case of leaving the correlated variables in the data file as well as removing them. The results of this work show that the highest success of classification objects into clusters is when the data set is cleaned of correlated variables. If the data file is not cleaned, the methods reach better results when the distance between objects is measured by Euclidean metric.

The aim of the thesis is to classify european countries in terms of their business climate using the method of cluster analysis over the years 2008-2013. Changes in classification during this period are associated with events of global significance (e.g. World financial crisis) or local importance (reforms, EU strategy...). Data base consists of indicators describing administrative, financial and law environment for doing business and are publicated by World Bank. Clusters indicate both geographic conditionality and specific attributes of these clusters, which suggest countries with better/worse conditions in some of the areas. Particular attention is given to development in the Czech republic. There was significant change in classificiation between 2008 and 2009 and subsequently became stable. The results of analysis correspond to the existing rankings and indicators of business demography. Potentials for improvement which might leed to stabel economic development according to the conducted analysis are outlined in the end of the thesis (f.e. implementation of unified administrative points, electronization and further simplification of bureaucratic processes).

Master thesis deals with dividing destricts of the Czech Republic in to clusters acording to demographic indicators during the year 2011. After the theoretical introduction with exploratory data analyiss, factor analysis and cluster analysis is described practical implementation of agglomerative hierarchical clustering. Within the frame cluster analysis we compare results calculated by four methods of clustering, which are the single linkage method, the complete linkage method, the average linkage method and Ward's method. At the conclusion we select the method which divides destricts of the Czech Republic in to the clusters the best. Master thesis includes a prezentations of discovere results with the help of dendrograms and cartograms. The analysis were carried out with the help of the statistical program STATISTICA.

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.

The master thesis deals with the problem of european energy security. The thesis covers also the cluster analysis, which assesses the current state of security in the individual member states. At the same time, the very last part of the thesis proposes measures to strengthen the EU's energy independence.

Nowadays taxes are the most important tax income of the state budget and at the same time they affect the standard of living of the population in various ways. Although the countries try to harmonize tax systems, there are still considerable differences between them. The aim of the thesis is to capture taxes in the Czech Republic from the point of view of statistics and then perform the comparison within the countries of the European Union. The thesis contains a brief characterization of the national accounts and the classification of the taxes in the national accounts according to the ESA 2010. It further deals with the description of the Czech tax system, including a definition of the individual taxes. The subsequent section captures the tax burden expressed by tax quota and the rest of the thesis is devoted to the cluster analysis, which is then applied to the data from the annual national accounts. Using cluster analysis, EU countries have been divided into groups according to similarities and differences in tax systems and in particular on the basis of the tax quota.

Cluster analysis is a popular method of multivariate statistics. Based on mutual similarities between objects this method is able to classify and divide objects into several groups or clusters. The results of the clustering can be different by using different methods, measures of distance and procedures. The main aim of this thesis is to compare the results of several methods of cluster analysis with the known classification of classes from the original data file. In total, there are 15 data files, which were analyzed and each of them contained known information about the right allocation of objects in groups. The success of clustering of each method was calculated by comparing the known classification of classes and resulted clusters. In addition to the comparison of individual methods of cluster analysis was compared the impact of standardization and correlation to the success of each method. To reflect the distance betweeen the objects within each clusters, squared Euclidean distance was used. The results of this thesis point out that better success of clustering were achieved in the case of correlated variables in data file. The succes of clustering was higher about 2 percent points than in the case when correlated variables were deleted from data set. The methods divided 69,8 % objects before standardization and 70,8 % objects after standardization. The results also show a large importance of standardization in the case of Ward´s method. After standardization this method rank the most objects into correct classification classes and were more succesful, about nine percent points. In the case of correlated variables is the succes of the method 76,4 %. Standardization positively influences also centroid method and the method of farthest neighbour. Median method, nearest neighbour method and the method of average linkage achieve higher success of clustering in the case of original, nonstandardized variables (uneven variables).

Objective of this thesis is cluster analysis of political parties and individual politicians in Chamber of Deputies of the Parliament of the Czech Republic. In theoretical part, legislative process in Czech Republic is explained and political situation in Chamber of Deputies is described. Also cluster analysis theory is explained. In practical part, firstly, cluster analysis of political parties was performed, than of individual members of parliament. The furthest neighbor and Ward's methods were used in this part. On the basis of discovered facts, it's possible to confirm compactness of coalition and also of conservative opposition. In specific political parties, as most united behave TOP 09 members and least ČSSD members of parliament.