This thesis summarizes latest findings about municipal solid waste (MSW) modelling. These are used to solve multivariable version of inverse prediction problem. It is not possible to solve such problem analytically, so heuristic framework using regression models and data reconciliation was developed. As a side product, models for MSW modelling using PCA (Principal Component Analysis) and LM (Linear Model) were created. These were compared with heuristic model called RF (Random Forest). Both of these models were also used for per capita MSW modelling. Theoretical parts about generalized linear models, data reconciliation and nonlinear programming are also included.

The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored  in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.

Modellen för röstmaximerande politiska partier i ett proportionerligt valsystem presenterad av Lindbeck och Weibull (1987) respektive Dixit och Londregan (1996) används för att testa svenska regeringens taktiska användning av de allmänna statsbidragen till kommunerna under åren 2007–2010. Resultaten tyder på att statsbidragen använts taktiskt vilket korroborerar teorin. Liknande analys för åren 2015–2018 kunde inte replikera resultaten. Svensk valundersökning används för att identifiera väljarnas ideologiska preferens för regeringen under tiden för riksdagsvalet 2006 varefter mätmodellen presenterad av Johansson (2003) och Dahlberg och Johansson (2002) används för att skatta poängen mot den latenta faktorn "ideologisk bias". Dessa poäng delas upp efter valkrets och används för att skatta den ideologiska biasens täthetsfördelning i respektive valkrets. Valresultatet används för att identifiera indifferenspunkterna där väljarna i respektive valkrets är indifferenta mellan båda politiska blocken. Täthetsfunktionerna utvärderas vid dessa punkter varefter betydelsen av tätheterna som determinant för mängden allmänna statsbidrag en kommun erhåller testas genom linjär regression. / A version of the model for voter share maximizing political parties in a proportional electoral system developed by Lindbeck and Weibull (1987) and Dixit and Londregan (1996) respectively is presented. This model is used to test if there is evidence for the tactical use of intergovernmental grants from the central incumbent government to the local governments at the municipal level during the first term of office 2007-2010 of the conservative Reinfeldt government in Sweden. The results show that such tactical use did occur, and which corroborates the theoretical framework for competing parties. Similar results were obtained for the periods 2011-2014 (conservative government) and 2015-2018 (socialist government). Data from the Swedish election studies are used to identify the voter’s ideological preferences for the incumbent central government during the time of the 2006 general election to the Swedish parliament. The theory behind the model presented by Johansson (2003) and Dahlberg and Johansson (2002) respectively was the guideline to estimate the factor scores against the latent factor “ideological bias”. The Gaussian kernel density function is used to estimate the ideological bias in each constituency: The actual election results are used to approximate the indifference cutpoint where the voters are indifferent between both political alternatives. The probability distribution functions were evaluated at these cutpoints after which the importance of these densities for the amount of intergovernmental grants a municipality receives is tested using linear regression.

The main subject of the diploma thesis is a market price modeling by real estates. As a tool for modeling, is used a multiple linear regression. As starting points, are used an econometrical theory and knowledge about real estate valuation. The main goal is to find optimal model for best capture in the time and place.

This master´s thesis deals with the creation of a model that explains the relationship between the amount of gross wages and selected economic factors in different regions of the Czech Republic. First part contains an explanation of terms of economic and statistical area. The second part deals with the creation of data basis and numerical model calculations. In this section the results of the analyzes are presented. The third part is focused on evaluation of the obteined results.

The master thesis deals with determination of selected biochemicals (lignin, carotenoids, water) content in Norway spruce needles using laboratory and imaging spectroscopy. The first part of thesis summarizes literature dealing with methods of estimating lignin and other biochemicals content. Three types of data are used in this thesis: 1. spectra measured by contact probe and ASD FieldSpec 4 Wide Res spectroradiometer, 2. spectra measured by integrating sphere and spectroradiometer and 3. aerial hyperspectral image data acquired by APEX sensor. The most useful transformation methods - first derivative and continuum removal are applied to the spectrum. Further the linear relationship between measured spectrum and content of biochemicals is analysed. Stepwise multiple linear regression is applied to select suitable wavelengths for modeling of biochemicals content in spruce needles. The model is also calculated and applied on the level of image hyperspectral data. Maps of lignin content in Norway spruce are the final output of these part of this. Next part of the thesis compares spectra measured by contact probe and spectra measured by integrating sphere. Diffrerence between the studied areas based on biochemicals content in spruce needles and several chemical elements in the soil and based on...

Die Kapitel 1 bis 6 im ersten Teil dieses Skriptes beruhen auf einer Vorlesung Ökonometrie I, die zuletzt im WS 2001/02 gehalten wurde, die Kapitel 7 bis 16 beruhen auf einer Vorlesung Ökonometrie II, die zuletzt im SS 2006 gehalten wurde. Das achte Kapitel enthält eine komprimierte Zusammenfassung der Ergebnisse aus dem Teil Ökonometrie I.

The migratory birds stop at different stopover sites during migration. The presence of resources in these stopover sites is essential to regain the energy of these birds. This thesis aims to compare the resource abundance and intake at the two stopover sites: Reda and Wisla river estuaries. How a bird's mass changes during its stay at an estuary is considered as a proxy for the resource abundance of a site. The comparison is made on different subsets, including those which has incomplete data, i.e. next day is not exactly one day after the previous capture. Multiple linear regression, Generalized additive model and Linear mixed effect model are used for analysis. Expectation maximization and an iterative predictive process are implemented to deal with incomplete data. We found that Reda has higher resource abundance and intake as compared to that of Wisla river estuary.

Infiltration and inflow of non-sewer water to wastewater systems (I/I water) poses a significant problem to health, economy, and the environment by affecting the capacity of the wastewater system. To determine effective measures to reduce the amount of I/I water, it is essential to identify its most important sources. In this thesis, the relationship between leaked drinking water and I/I water was investigated by using ordinary least squares (OLS) linear regression. The first question is whether there is a statistical relationship between the amount of drinking water leakages and the amount of I/I water in wastewater treatment plants in Swedish municipalities. Data from 207 Swedish municipalities were included in the analysis. The amount of unaccounted water, i.e. the difference between produced and charged drinking water, was used as a measure of water leakage. The second question treats the feasibility of statistically comparing municipalities to determine the importance of I/I water sources. The result of the OLS linear regression shows a statistically significant relationship between the level of unaccounted water and the level of I/I water in Swedish municipalities. This could be explained by a causal process where leaked drinking water infiltrates the wastewater system. Another explanation could be simultaneous problems in the water system and the wastewater system, potentially caused by the quality of the pipelines. However, caution should be exercised when drawing conclusions from statistical comparisons between water and wastewater flows in different municipalities. Variations in other aspects such as natural conditions and infrastructure, must also be considered.

El presente trabajo de investigación tiene como objetivo aplicar técnicas de análisis de regresión y aprendizaje automático (ML) para mejorar los resultados de estimación de sobre dilución en tajos explotados por el método de Sub Level Stoping (SLS) de la Compañía Minera Condestable (CMC) a través de la generación de ecuaciones de regresión y código en lenguaje de Python para las técnicas de ML. Para la estimación de sobre dilución se analizaron las reconciliaciones de tajos explotados con el método de SLS del período 2017-2019 con la aplicación de las técnicas: Análisis de Regresión Lineal Múltiple (ARLM), regresión no lineal múltiple (ARNM) y métodos de aprendizaje automático (ML) como Máquinas de Vectores de Soporte (SVM) y bosques aleatorios (RF), lo que permitió establecer comparaciones entre los resultados a nivel predictivo y tecnológico con la metodología de O’Hara aplicada actualmente en CMC para la estimación de sobre dilución de tajos SLS. La aplicación de las técnicas mencionadas implicó variables operativas como: nivel, buzamiento, densidad, burden, espaciamiento, altura, longitud, ancho, RQD, RMR y ratio de tonelada por metro de perforación (TMP) de los tajos evaluados, mientras que el objetivo o variable dependiente fue la sobre dilución. Ello permitió inicialmente identificar que las técnicas de regresión ARLM y ARNM mejoraron el coeficiente de determinación R2 de O’Hara en 5.5% y 4.4%. Luego, con la aplicación de herramientas de aprendizaje automático se identificó que ambas técnicas (SVM y RF) lograron la mejora en 0.3% y 18.5% respectivamente. El resultado de ello fue la reducción de la diferencia de costos estimados obtenidos con la metodología de O’Hara relacionados al costo adicional por carguío y transporte de carga rota de dilución. / This research work aims to apply Regression Analysis and Machine Learning (ML) techniques to improve the results of estimating over dilution in stopes mined by Sub Level Stoping (SLS) method at Compania Minera Condestable (CMC) through the generation of regression equations and code in Python language for ML techniques. For the estimation of over dilution, the reconciliations of stopes mined with the SLS method for the period 2017-2019 were analysed with the application of the techniques: Multiple Linear Regression Analysis (MLRA), Multiple Non-linear Regression Analysis (MLNRA) and Machine Learning (ML) methods such as Support Vector Machine (SVM) and Random Forests (RF), which allowed comparisons of the results at predictive and technological level with the O'Hara methodology currently applied at CMC for the estimation of over dilution of SLS stopes. The application of the afore mentioned techniques involved operational variables such as: level, dip, density, burden, spacing, height, length, width, RQD, RMR and tonne per metre drilling (TMP) ratio of the evaluated stopes, while the objective or dependent variable was over dilution. This initially identified that the ARLM and ARNM regression techniques improved O'Hara's R2 determination coefficient by 5.5% and 4.4%. Then, with the application of machine learning tools it was identified that both techniques (SVM and RF) achieved the improvement by 0.3% and 18.5% respectively. This resulted in a reduction of the estimated cost difference obtained with the O'Hara methodology related to the additional cost of loading and transporting broken stock from the dilution. / Tesis