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Modern statistical approach in evaluating the compressive strength of concrete in structures using the rebound hammer method
Janka, Marek ; Kocáb, Dalibor (referee) ; Misák, Petr (advisor)
This diploma thesis examines various linear regression methods and their use to establish regression relationships between the compressive strength of concrete determined by the indirect method and by the crushing of the specimens in the press. It deals mainly with the uncertainty of values measured by the indirect method, which is neglected by the usually used ordinary least squares regression method. It also deals with the weighted least squares method, suitable for so-called heteroskedastic data. It compares different regression methods on several sets of previously measured data. The final part of the work examines the effect of removing too influential points identified by Cook's distance, which may skew the regression results.
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Technical Analysis
Kotásek, Lukáš ; Šustrová, Tereza (referee) ; Novotná, Veronika (advisor)
Thesis is focused on CAPM model. Characteristics of five chosen stocks of NASDAQ capital market are calculated using linear regression. Market index S&P 500 is used to represent the market. Calculated values are verbally interpreted. As a presented solution, a program is created in VBA language, that will help the user to convert historical stock data from Yahoo! Finance to form, that can be used for calculations. The program will then use the data to calculate said characteristics. Program is also tested on function and performance using larger amounts of historical data.
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Creation of New Prediction Units in Data Mining System on NetBeans Platform
Havlíček, David ; Bartík, Vladimír (referee) ; Lukáš, Roman (advisor)
The issue of this master's thesis is a creation of new prediction unit for existing system of knowledge discovery in database. The first part of project deal with general problems of knowledge discovery in database and predictive analysis. The second part of the project deal with system developed on FIT, for which is module implemented, used technologies, concept and implementation of mining module for this system. The solution is implemented in Java language and is a built on the NetBeans platform.
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Computational tasks for Parallel data processing course
Horečný, Peter ; Rajnoha, Martin (referee) ; Mašek, Jan (advisor)
The goal of this thesis was to create laboratory excercises for subject „Parallel data processing“, which will introduce options and capabilities of Apache Spark technology to the students. The excercises focus on work with basic operations and data preprocessing, work with concepts and algorithms of machine learning. By following the instructions, the students will solve real world situations problems by using algorithms for linear regression, classification, clustering and frequent patterns. This will show them the real usage and advantages of Spark. As an input data, there will be databases of czech and slovak companies with a lot of information provided, which need to be prepared, filtered and sorted for next processing in the first excercise. The students will also get known with functional programming, because the are not whole programs in excercises, but just the pieces of instructions, which are not repeated in the following excercises. They will get a comprehensive overview about possibilities of Spark by getting over all the excercices.
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Quality of The Track Geometry Progress
Nejezchlebová, Jitka ; Kulich, Pavel (referee) ; Svoboda, Richard (advisor)
The bachelor ’s thesis deals with the development of track geometry over time. The theoretical part describes the methodology for measuring and evaluating of track geometry. The practical part deals with the section evaluation on the double-track line Brno Maloměřice - Adamov. The MATLAB system was used for evaluation. For chosen sections, the graphs were regressed to determine the quality development for all parameters. Regressions were also used to determine which parameter deteriorates the fastest over time, in which track the parameters deteriorate faster and whether it can be said that the increase is faster before or after the repair.
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Regression Analysis of Spatially and Time Distributed Data
Rosecký, Martin ; Hübnerová, Zuzana (referee) ; Bednář, Josef (advisor)
This thesis summarizes findings about municipal solid waste (MSW) forecasting. Basic information about linear regression and correlation analysis were described. Analysis of influencing factors was realized on municipality with extended competence level. The resulting models explain up to 99 % of variability. Final models of MSW per capita explain between 12 and 75 % of variability. Variability explained by model of MSW per capita is lower by 20 % than comparable study which however uses data that are not usually available. Models can be used in waste management and their simplicity is benefit for real usage.
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Gender Recognition from Photograph
Kałuża, Marian ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This paper presents a multiresolution approach for gender recognition based on Histogram of Oriented Gradients and Local Binary Patterns. The experiment showed that gender recog- nition accuracy can be improved not only by aquiring different features on the same image resolution but even by gathering just a single feature at different image scales. The pre- sented approach is quite competitive with above 95% accuracy in both evaluated datasets.
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Statistical analysis of big industrial data
Zamazal, Petr ; Popela, Pavel (referee) ; Šomplák, Radovan (advisor)
This thesis deals with processing of real data regarding waste collection. It describes select parts of the fields of statistical tests, identification of outliers, correlation analysis and linear regression. This theoretical basis is applied through the programming language Python to process the data into a form suitable for creating linear models. Final models explain between 70 \% and 85 \% variability. Finally, the information obtained through this analysis is used to specify recommendations for the waste management company.
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The Introduction and Application of General Regression Model
Hrabec, Pavel ; Štarha, Pavel (referee) ; Bednář, Josef (advisor)
This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.
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