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Assistance services within car insurance and accident insurance
Musilová, Klára ; Bílková, Diana (advisor) ; Makhalova, Elena (referee)
The bachelor thesis is focused on selected companies providing services in the field of roadside assistance in the Czech Republic. It will describe only companies belonging to the Association of Czech assistance companies. Then they will be matched by various factors. The work will be also focused on the current topic of crash hunters. There will be used different statistical methods data relating to assistance services from unnamed assistance company. It will be analyzed by using selected characteristics of descriptive statistics and a one-way analysis of variance. Then will be done the questionnaire survey regarding to assistance services. Respondents will be Czech citizens owning a driver license. Then data obtained by interviews will be evaluated. Some of the findings in the survey will be analyzed by using chi-square test in combination table, due to dependencies on knowledge assistance services from the point of view of clients according to the length of ownership of the driver license.
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Comparison of the development of the telecommunications market in the Czech Republic in the years 2002-2012
Khmelevskiy, Vadim ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The aim of this work is to evaluate the economic success of three major mobile operators on the Czech market (T-Mobile, Vodafone and O2), compared with each other to analyze their development in the period preceding the financial crisis, in the time of the crisis and in the time of recovery from the crisis. All necessary data were taken from the annual reports for the years 2002 to 2012. The work is divided into two chapters. The first part will be devoted to financial analysis, from which it will be possible to assess the financial health of the company. The second part will focus on the analysis of time series data of some selected indicators of the company, particularly the profit after tax of equity and total liabilities. Based on the use of appropriate statistical methods, the prediction of the future development of the indicators of profit after tax will be carried out.
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The comparison of unemployment in the Central Bohemia region and in the Moravia-Silesia region
Vitoušová, Dana ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
Unemployment is one of the biggest economic and social problems these days. The bachelor thesis compares unemployment in the Central Bohemia region and in the Moravia-Silesia region. The thesis is composed of a theoretical and an analytical part. In the theoretical part are characterized both regions described unemployment and some indicators e. g. level of economic activity, unemployment rate, dynamics rate and others. The analytical part compares both regions from different perspectives using data from Czech Statistical Office and Ministry of Labour and Social Affairs. The analysis uses appropriate statistical methods. This bachelor thesis provides a comprehensive overview of unemployment in the Central Bohemia region and in the Moravia-Silesia region.
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Evaluation of the Success of Coefficients and Methods Used in Cluster Analysis
Hammerbauer, Jiří ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The diploma thesis explores with the evaluation of the success of selected indices for determining the number of clusters used in cluster analysis. The aim of this thesis is on the basis of various combinations of clustering methods and distances verify whether, alternatively using which clustering methods and distances is it possible to rely on the results of indices for determining the number of clusters. The results of success rate presented in the third chapter suggest that not all of indices for determining the number of clusters can be used universally. The most successful index is Dunn index, which was able to determine the correct number of clusters in 37 % of cases, respectively Davies-Bouldin index with the share of 70 % when including deviation of one cluster. The success rate is affected by both used method and selected distance.
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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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Scoring methods used in cluster analysis
Sirota, Sergej ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part are described and analyzed 20 files. Each file contains only quantitative variables and sort characters by which objects are sorted. In each file is calculated success rate of object segmentation into groups for each cluster method. At the end of the practical part is a summary description of the results of cluster methods. The main contribution of this thesis is to evaluate the success of cluster methods for classification objects into known groups.
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