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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Modeling of the Mortality Development in the Czech Republic
 Hejdová, Martina ; Zimmermann, Pavel (advisor) ; Čech, Tomáš (referee)
The thesis deals with a mortality modelling in the Czech Republic. The main aim of this thesis is to describe historical development of mortality in the Czech Republic on the basis of multinomial regression. In a theoretical part the basic knowledge demanded for model construction divided into two chapters can be found. In the first chapter the basic difference between linear and generalized linear model is described. Second chapter is devoted to a logistic regression. Here we proceed from simple, binomial variable and then we generalize it for multinomial variable. Practical part deals with a construction of a described model. In the third chapter data (type and source) are described and in the fourth chapter characterizes all three models itself (two of them are descriptive and the third one is used for simple prediction). The prediction is added rather for completeness than for the importance of the work itself because it was not the main goal.
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The use of logistic regression in the market research
 Brabcová, Hana ; Pecáková, Iva (advisor) ; Ranocha, Pavel (referee)
The aim of this work is to decide the real usage of logistic regression in the market research tasks respecting the needs of final users of research results. The main argument for the final decision is the comparison of its output to the output of an alternative classification method used in practice -- a classification tree method. The topic is divided into three parts. The first part describes the theoretical framework and approaches linked to logistic regression (chapter 2 and 3). The second part analyses the experience with the usage of logistic regression in Czech market research companies (chapter 4) and the topic is closed by applying the method on real data and comparing the output to the classification tree output (chapter 5 and 6).
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