This thesis investigates the combination of changes in the population and employment into sectors in rural Sweden for the period 1960-2010. The aim is to describe and analyze the demographic changes together with the labour market changes, and to account for the spatial outcome of these changes by considering the heterogeneity of rural areas. The analysis departs from the framework of rural restructuring, where changes in employment and population in rural Sweden are interpreted as local products of the global processes of technological development, social modernization and globalization. Empirically, the analysis is based on a combination of longitudinal censuses and register data on the Swedish population covering the period 1960-2010. The first part of the aim is achieved by applying a life-course perspective and exploiting the longitudinal nature of the data. The life-course perspective distinguishes between historical time and the age of individuals, making it possible to situate changes in employment and migration on the individual level. The second part of the aim is achieved through developing a typology of rural Sweden by doing a cluster analysis on SAMS-areas. The results show that rural change after 1980 was characterized by de-industrialization and the rise of the urban service sector. The period was also characterized by regional urbanization rather than local urbanization. Peripheral urban and rural areas based on industrial employment found themselves with a declining economic motor, which meant that people had to find their source of income elsewhere. The migration stream in this period was thus increasingly directed towards metropolitan or large city centers, and their rural surroundings within commuting distance. However, the more fine-tuned spatial typology reveals that also a few areas in the rural periphery have experienced growth, these areas are mainly attractive places based on various kinds of tourism. It can thus be concluded that different rural areas have experienced, and will continue to experience, the shift from manufacturing to services differently, where some areas have grown in both demographic and employment terms while others have declined. In this sense the heterogeneity of rural areas are a product of both growth and decline – of old development paths that is reaching their end and of new development paths that will continue into the future.

Tax administrations worldwide provide taxpayers with favour and offer them the opportunity to set up transfer pricing system according to pre-agreed criteria. When taxpayer´s system complies with, a tax administrator does not dispute the transaction. Any predefined arrangement leading to the recognition of transfer pricing system by the tax administrator can be defined as a safe harbour. It was found out during the review process of current sources, that there is no relevant research available focused on assessing the appropriateness of introducing safe harbour system for both businesses and government. At the same time, even the OECD does not work with data that would conclusively prove the impact of transfer pricing regulation on companies´ behaviour. For this reason, a research was conducted in this area with focus on the potential of safe harbour to affect tax contributions of enterprises. Financial data were rendered from Amadeus database. The impact of safe harbour system was observed on the time series development of taxation of particular cluster. Cluster analysis was selected as the most suitable method for grouping companies.

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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.