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Classification of European countries based on their business climate
Pospíchalová, Barbora ; Löster, Tomáš (advisor) ; Pivoňka, Tomáš (referee)
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).
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The evaluation of coefficients when determining the optimal number of clusters in cluster analysis
Novák, Miroslav ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
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.
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Evaluating the success of cluster analysis methods
Maršálková, Kateřina ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
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.
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Analysis of household consumption in the EU
Kolman, Martin ; Bílková, Diana (advisor) ; Malá, Ivana (referee)
The goal of this work is to analyze the evolution of household consumption of the states in the EU. The consumption will be researched in the view of classification COICOP, which is the classification of individual consumption by purpose. After mapping of this evolution the estimation of future values will be done from known time series. This estimation will be performed by two different ways. First one will respect the composition of household consumption in sections of classification COICOP. The second one will only work with time series of average consumption for all sections together. To compare the states cluster analysis will be done. This analysis will be done by two ways again. First one will be aimed to analyze the current situation and the second one will be aimed to analyze the evolution of household consumption. Instead of Microsoft Excel STATGRAPHICS X64 CENTURION and SPSS will be used in this thesis. Household consumption prognosis is the main benefit of this thesis. This prognosis is made for all sections of COICOP. Analysis has shown, that the consumption should rise in future. There are few exceptions, mainly countries with not good economic situation as Greece.
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The use of statistical methods in data mining in predicting consumer behaviour for Internet purchases
Podzimková, Michaela ; Vilikus, Ondřej (advisor) ; Berka, Petr (referee)
Data mining is a new discipline that occurs with increasing amount of stored data and the increasing need to obtain the information hidden in them. It is focused on the mining of potentially useful information from large data sets and it lies at the intersection of statistics, machine learning, artificial intelligence, databases and other areas. The aim of this thesis is to present the process of data mining with an emphasis on its connection with statistics and to describe a selection of statistical methods widely used in this field and which were also used in the applied data mining problem in this thesis. Real data from purchases in the online store show that using different methods gives different results and interesting information about purchasing behavior, and also proves that not all methods are always applicable to all types of tasks.
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Factors influencing the financial situation of Ph.D. students in the Czech Republic
Zahradníčková, Jana ; Vltavská, Kristýna (advisor) ; Stoklasa, Jan (referee)
Ph.D. students are an integral part of the tertiary education system. Encouragement for doctoral programs and their students is very important because they are the ones who will participate in research projects in the future and they will contribute to society as a whole. The majority of scholarships for Ph.D. students comes from public sources. An important question to be asked is whether the scholarships are sufficient to finance Ph.D. studies and whether there are differences in the amount depending on gender, field of study or region. This thesis aims to answer these questions by applying statistical methods to the results of the survey DOKTORANDI 2014.
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Book market segmentation
Jarošová, Jana ; Koudelka, Jan (advisor) ; Vrančíková, Marie (referee)
This thesis is focus on the book market in Czech Republic and its current situation. The main objective of the thesis is, through primary research and cluster analysis, to uncover market segments, which shows significant differences in costumer behavior and through crosstabs develop profile of these groups. Base on this information focus on the segments attractive for bookshops to address them and prepare recommendation to focus on them. The market research has a qualitative character and is realized inform of personal and internet questionnaire. Selection of respondent is in form of Quota sampling, using quotas tender, age and education.
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Analysis of expenditures of households on culture focused on film industry
Procházková, Romana ; Vltavská, Kristýna (advisor) ; Hanzlík, Jan (referee)
The aim of this work is a statistical analysis of expenditures of households on culture, description of current economical situation in the field of cinematography, characteristics of a cinemagoer and a projection of future developmental tendencies of cinemas. The first two chapters are dedicated to a description of the current state of the Czech cinematography and information about the evolution of the Czech film production. The third theoretical part describes basic statistical methods used in the practical part of this work. The fourth part is focused on a film viewer -cinemagoer using MML-TGI method and it also includes analysis of the dependence of expenditures of households for culture on the cinema ticket price. The fifth part is dedicated to a projection of the future development of the attendance and receipts of cinemas. The final chapter deals with the state of European cinematography.
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Segmentation of Infant Food Market
Polášková, Kateřina ; Koudelka, Jan (advisor) ; Zamazalová, Marcela (referee)
The main goal of my master thesis is to identify differences, or similarities among consumers of infant food. Based on these characteristics were revealed groups, also called segments. Consumers in these segments are as homogeneous as possible, and vice versa the segments are mutually the most heterogeneous. After uncovering these segments is another goal of proposing marketing recommendations to individual segments. The work consists of theoretical and practical part. The theoretical part describes the process of segmentation. Practical part deals with the uncovering of segments. The research used secondary and primary data. Were used a secondary data from the database MML-TGI, which was analyzed by the Data Analyzer. Primary data was collected by questionnaire survey and subsequently processed in the IBM SPSS Statistics version 22. There has been uncovering the four segments, which have been named, described and suggested marketing recommendation.
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Segmentation of the coffee market
Tůma, David ; Koudelka, Jan (advisor) ; Petruška, Tomáš (referee)
This Master's Thesis is focused on analysis of segmentation of the coffee market in the Czech Republic based on similarities and differences between users of coffee. Uncovering segments and development was carried out in a software program Data Analyzer and PASW using data from primary research and unique agency research MML-TGI of company Median s.r.o. The theoretical part presented problems, approaches, method of segmentation and types of coffee. The practical part starts with analyzing and defining the market in the Czech Republic. The target group of users was subsequently characterized by a general analysis. Variables were reduced to the factors by using factor analysis. Then users were put into clusters based on cluster analysis. Segments were characterized in detail using cross-analyzes and multivariate statistical methods. Content analysis expands a current picture of the segments on which other companies target. Finally, there were elaborated appropriate marketing recommendations for the individual segments to their effective positioning.
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