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CHAID and logistic regression
Novák, Jaroslav ; Čabla, Adam (advisor) ; Matějka, Martin (referee)
The aim of this thesis is to introduce logistic regression and method dedicated to construction of decision trees called CHAID, compare these two methods with regard to interpretation of their outputs. In order to accomplish the set goals application of these methods will be presented on real dataset. Statistical software will be used to obtain outputs. The outputs will be interpreted and conclusions on their bases will be presented. This thesis will also introduce possibilities of interpretation of these outputs and pros and cons that are connected with them.
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On-line Services in Hospitals
Venkrbcová, Eva ; Bína, Vladislav (advisor) ; Lešetický, Ondřej (referee)
The thesis is focused on mapping of selected on-line services in hospitals in Czech Re-public according to their availability. In the next step logistic regression model will be used, which will measure a range of particular on-line services according to their legal form, majority ownership, region in which health organization is located, to education achieved and gender of hospital director and lastly in accordance with the size of hospital from the viewpoint of bed capacity. This thesis will provide comprehensive overview of availability of on-line services in Czech hospitals and determine which factors significantly decreasing or increasing chance of their occurrence on hospital websites.
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Data Mining and use of decision trees by creation of Scorecards
Straková, Kristýna ; Witzany, Jiří (advisor) ; Fičura, Milan (referee)
The thesis presents a comparison of several selected modeling methods used by financial institutions for (not exclusively) decision-making processes. First theoretical part describes well known modeling methods such as logistic regression, decision trees, neural networks, alternating decision trees and relatively new method called "Random forest". The practical part of thesis outlines some processes within financial institutions, in which selected modeling methods are used. On real data of two financial institutions logistic regression, decision trees and decision forest are compared which each other. Method of neural network is not included due to its complex interpretability. In conclusion, based on resulting models, thesis is trying to answers, whether logistic regression (method most widely used by financial institutions) remains most suitable.
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Using data mining methods in the analysis of credit risk data
Tvaroh, Tomáš ; Witzany, Jiří (advisor) ; Matejašák, Milan (referee)
This thesis focuses on comparison of selected data mining methods for solving classification tasks with the method of logistic regression. First part of the thesis briefly introduces data mining as a scientific discipline and classification task is shown in the context of knowledge data discovery. Next part explains the principle of particular methods amongst which, along with logistic regression, artificial neural networks, classification decision trees and Support Vector Machine method were selected. Together with mathematical background of each algorithm, demonstration of how the classification functions for new examples is mentioned. Analytical part of this thesis tests decribed methods on real-world data from the Lending Club company and they are compared based on classification accuracy. Towards the end, an evaluation of logistic regression is made in terms of whether its majority position is due to historical reasons or for its high classification accuracy compared to other methods.
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The development of the situation of juniors and seniors
Siegelová, Klára ; Bartošová, Jitka (advisor) ; Bína, Vladislav (referee)
The final thesis deals with social situations juniors and seniors in selected countries of the European Union. The thesis monitors changes in social developments primarily in terms of income, education, and especially of unemployment. The selected period is the period from approximately 2005 to 2011, in some cases up to 2013. The aim of this thesis is the statistical analysis of the data set EU-SILC for 2005 and 2010 of Czech Republic, Slovakia, Poland, Germany, France and Spain with focusing on income, education and unemployment among age groups.
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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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Building a predictive model for bankruptcy
BÜRGER, Pavel
Thesis deals with complex process of creation of new bankruptcy model for predicting business failure, while this process involves selection of quality sample, verification of classification accuracy of already existing bankruptcy models, profile analysis and finally the derivation of specific equation of bankruptcy model. The derivation is performed by using two selected statistical methods, discriminant analysis and logistic regression. Two bankruptcy models Bürger's index DA12 and Bürger's index LR12 were derived by using the mentioned statistical methods. The new models distinct advantage is, unlike already existing and renowned bankruptcy models, that they are focused on classification of micro and small enterprises in terms of Czech Republic, while classification accuracy one year before failure is by individual models 74.8 % and 81.87 %. Derived models have clear interpretation (no grey zone) and easy calculation, which brings a possibility for micro and small entrepreneurs to check their business partners in terms of failure prediction.
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Financial distress prediction of company
MAŇASOVÁ, Helena
The theoretical part of this master thesis deals with creation and solution of financial distress and analysing classification models. In the practical part I defined own methods for financial distress prediction of company using discriminant analysis and logistic regression.
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The Risk of Poverty in the Czech Republic
Klein, Jan ; Bartošová, Jitka (advisor) ; Bína, Vladislav (referee)
The goal of this work is to identify and analyse factors with impact on the income decrease of households under the poverty line. Data used in this work are taken from EU SILC survey. In this work is created a statistical model which help us to discover relevant and irrelevant factors. The situation and it's development is analysed only for Czech households in this work
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