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Analysis of monthly time series of temperatures at chosen stations in Europe
Henkrichová, Jana ; Helman, Karel (advisor) ; Řezanková, Hana (referee)
The bachelor thesis is focused on the analysis of monthly time series of temperatures at nine meteorological stations in Europe between the years of 1951 and 2015. The data were obtained from the database of The European Climate Assessment & Dataset. The geographical position data of chosen stations consist of points in an imaginary grid throughout Europe. The purpose of this bachelor thesis is to compare the development of time series via basic characteristics, to explore the differences due to geographical position and to gain some information about the development of temperature in time via statistical methods. The main object of interest is the location of Praha - Klementinum. Some additional goals are testing the measure of independence of temperatures on the geographical latitude, verifying the continental influence on the climate and checking the shapes of distributions of temperature time series.
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Valuation of real estates using statistical methods
Funiok, Ondřej ; Pecáková, Iva (advisor) ; Řezanková, Hana (referee)
The thesis deals with the valuation of real estates in the Czech Republic using statistical methods. The work focuses on a complex task based on data from an advertising web portal. The aim of the thesis is to create a prototype of the statistical predication model of the residential properties valuation in Prague and to further evaluate the dissemination of its possibilities. The structure of the work is conceived according to the CRISP-DM methodology. On the pre-processed data are tested the methods regression trees and random forests, which are used to predict the price of real estate.
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Hodnocení Výsledků Fuzzy Shlukování
Říhová, Elena ; Pecáková, Iva (advisor) ; Řezanková, Hana (referee) ; Žambochová, Marta (referee)
Cluster analysis is a multivariate statistical classification method, implying different methods and procedures. Clustering methods can be divided into hard and fuzzy; the latter one provides a more precise picture of the information by clustering objects than hard clustering. But in practice, the optimal number of clusters is not known a priori, and therefore it is necessary to determine the optimal number of clusters. To solve this problem, the validity indices help us. However, there are many different validity indices to choose from. One of the goals of this work is to create a structured overview of existing validity indices and techniques for evaluating fuzzy clustering results in order to find the optimal number of clusters. The main aim was to propose a new index for evaluating the fuzzy clustering results, especially in cases with a large number of clusters (defined as more than five). The newly designed coefficient is based on the degrees of membership and on the distance (Euclidean distance) between the objects, i.e. based on principles from both fuzzy and hard clustering. The suitability of selected validity indices was applied on real and generated data sets with known optimal number of clusters a priory. These data sets have different sizes, different numbers of variables, and different numbers of clusters. The aim of the current work is regarded as fulfilled. A key contribution of this work was a new coefficient (E), which is appropriate for evaluating situations with both large and small numbers of clusters. Because the new validity index is based on the principles of both fuzzy clustering and hard clustering, it is able to correctly determine the optimal number of clusters on both small and large data sets. A second contribution of this research was a structured overview of existing validity indices and techniques for evaluating the fuzzy clustering results.
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Segmentation of business company customers using cluster analysis methods
Nesrstová, Markéta ; Řezanková, Hana (advisor) ; Vrabec, Michal (referee)
This thesis discusses the possibilities of using cluster analysis methods for customer segmentation. The theoretical part is focused on description of selected methods of cluster analysis and explanation of other concepts related to this topic, such as CRM, segmentation and targeted communication. In the practical part are applied cluster analysis methods to real data unnamed company with the aim of creating a default substrates useful for planning and implementation of targeted communication. For the main calculations is used program R, for data and output editing is used MS Excel. At the end of the work are evaluated applied methods and summarized lessons learned from the cluster analysis. For a company were created and characterized databases which could be useful for marketing decisions.
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Míry podobnosti pro nominální data v hierarchickém shlukování
Šulc, Zdeněk ; Řezanková, Hana (advisor) ; Šimůnek, Milan (referee) ; Žambochová, Marta (referee)
This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.
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Use of regression for analysis of attendance of individual teams from NHL
Turek, Tomáš ; Řezanková, Hana (advisor) ; Vrabec, Michal (referee)
This bachelor thesis focuses on an analysis of regression which is concerned with the average of the spectator attendance in home games of individual teams from National Hockey League in the 2014/2015 season. The aim of this thesis is to consider the selected factors which might have an influence over the increase and the decline of attendance and the comparison to the results of selected regression methods at the selection of the variables to the regression model. The main benefit of this bachelor thesis is in the practical application of the analysis of regression including the selection of the best set of the independent variables with the utilization of various regression methods. A part of thesis is also a factual interpretation of the obtained results. For the selection of independent variables was used the stepwise method, forward and backward method.
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Voting analysis of the Chamber of Deputies
Zubatý, Radek ; Vrabec, Michal (advisor) ; Řezanková, Hana (referee)
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.
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Analysis of factors influencing relative market stock valuation
Hanzl, Tobiáš ; Vrabec, Michal (advisor) ; Řezanková, Hana (referee)
The goal of this diploma thesis is to analyze P/S ratio using Gordon dividend discount model and also to prove hypothesis that assumes existing influence of margin, dividend payout ratio, future dividend growth and discount rate on P/S ratio value. The goal is also to find other factors that can influence relative market stock valuation. Multidimensional regression analysis and also factor analysis were used in order to get a proper knowledge of the factors. There are 781 stocks used in this work. This thesis proves influence of the mentioned variables and also other variables were found that help achieve deeper understanding of examined variable. Market valuation is a very complex matter and is influenced by numerous factors.
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Analysis of voting in the Chamber of Deputies
Ventruba, Štěpán ; Vrabec, Michal (advisor) ; Řezanková, Hana (referee)
This bachelor thesis investigates voting of members and political groups of the Parliament's Chamber of Deputies. Descriptive statistics, contingency analysis and chi-square tests were used to explore the existence and intensity of dependencies in voting patterns of deputies and political groups, their activity, intragroup agreement, intergroup agreement and accordance of their voting with the voting patterns of coalition government and opposition. The results reveal a significant effect of changes in rules of procedure on the perception of activity of individual members.
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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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