This study sought to determine the effect of varying degrees of disproportionality of four methods of handling disproportionality cell frequencies in two-way analysis of variance. A Monte Carlo simulation procedure was employed. Two multiple linear regression techniques and two "approximate" techniques were compared.

This diploma thesis deals with Cloud computing, in particular its implementation of Microsoft's Windows Azure platform. The following objectives were defined for the thesis, which is analyzing and identifying the essential characteristics of Cloud computing with a focus on the Windows Azure platform, characterizing the key differences of applications in the cloud than on-premise applications, formulate recommendations for enabling transformation of existing applications into the Microsoft Cloud computing, which is delivered as a service. The main contribution of the work is to identify key differences between Windows Azure and on-premise solution. Creating recommendations to minimize the negative impact of these differences and maximize the benefits associated with the use of Windows Azure Cloud Computing platform.

The aim of the thesis on the topic of High-Performance Analytics is to gain a structured overview of solutions of high performance methods for data analysis. The thesis introduction concerns with definitions of primary and secondary data analysis, and with the primary systems which are not appropriate for analytical data analysis. The usage of mobile devices, modern information technologies and other factors caused a rapid change of the character of data. The major part of this thesis is devoted particularly to the historical turn in the new approaches towards analytical data analysis, which was caused by Big Data, a very frequent term these days. Towards the end of the thesis there are discussed the system sources which greatly participate in the new approaches to the analytical data analysis as well as in the technological solutions of High Performance Analytics themselves. The second, practical part of the thesis is aimed at a comparison of the performance in conventional methods for data analysis and in one of the high performance methods of High Performance Analytics (more precisely, with In-Memory Analytics). Comparison of individual solutions is performed in identical environment of High Performance Analytics server. The methods are applied to a certain sample whose volume is increased after every round of executed measurement. The conclusion evaluates the tests results and discusses the possibility of usage of the individual High Performance Analytics methods.