Introdução - A maior limitação para avaliar a dieta habitual é dada pelo erro de medida. Para minimizar os seus efeitos, tem-se proposto metodologias de calibração para correção dos dados e medidas associativas, consistindo na determinação de uma relação entre duas escalas de medida, utilizando-se regressão linear. Objetivo - Calibrar os dados obtidos por Questionário de Freqüência Alimentar para Adolescentes (QFAA), a partir do fator de calibração obtido por regressão linear. Metodologia - A amostra foi constituída por 74 adolescentes de ambos os sexos, alunos de uma escola pública de Piracicaba, com idade entre 10 e 14 anos. Foram excluídos indivíduos com idade superior ou igual a 14 anos ou com dados de consumo de energia não plausíveis. Obtiveram-se informações sobre dados socioeconômicos, antropométricos, demográficos e de maturação sexual. O consumo alimentar foi levantado por meio de Questionário de Freqüência Alimentar para Adolescentes e dois Recordatórios de 24 horas. Os dados de consumo de ambos os instrumentos foram ajustados pela energia, sendo que apenas os dados do recordatório foram ajustados pela variabilidade intrapessoal. Realizaram-se análises descritivas e de tendência central, one way ANOVA, coeficientes de correlação de Pearson e regressão linear. A média dos dois recordatórios foi utilizada como referência para a calibração dos dados. Resultados - De 74 indivíduos, 71,6% eram do sexo feminino. As médias das variáveis dietéticas foram muito semelhantes para o questionário calibrado e média dos recordatórios, com redução dos valores de desvio-padrão. Os coeficientes de calibração da regressão linear variaram de -0,05 (ferro) a 0,28 (vitamina C). Conclusão – Pelos resultados encontrados, a metodologia utilizada para a calibração dos dados dietéticos foi capaz de reduzir o erro de mensuração e, mesmo não o eliminando por completo, é uma abordagem que pode ser utilizada para obter estimativas menos enviesadas. / Background - A major limitation in usual diet assessment is the measurement error. Calibration approaches have been proposed to minimize its effects and to correct risk estimates. Calibration could be defined as a method which determines a relation between rank orders of two instruments by linear regression. Objective – to apply a calibration strategy in nutrient intake datas obtained by Adolescents Food Frequency Questionnaire (AFFQ), by using a calibration factor obtained by linear regression. Methodology – 74 boys and girls (10 to 14 years old) enrolled at a public school of Piracicaba were assessed. Values of energy intake higher than 6000Kcal and adolescents older than 14 years were excluded. Demographic and anthropometric data, sexual maturation and dietary intake (assessed by food frequency questionnaire and 24-hour recall) were examined. Dietary data intakes were adjusted by energy and, only 24-hour recall data were adjusted by within-person variance. Descriptive statistics, one way ANOVA, Pearson correlation coefficients and linear regression were performed. Results - 71,6% were girls. The calibrated values were similar to the reference data, with a reduction of standard deviation values. Linear regression coefficients (λ) ranged from -0,05 (iron) to 0,28 (vitamin C). Conclusion – The methodology used to calibrate dietary data was capable to reduce measurement error. Although it was not able to eliminate error completely, it is an approach that can be used to obtain less unbiased estimates.

This master thesis deals with the analysis of housing prices in Prague. The main goal is to identify and explain the factors which have an influence on the prices of flats at the macro and micro level. Two spatial statistic methods, namely multiple linear regressions and geographically weighted regressions (GWR), are used in the first part of the thesis, which deals with the prices in Prague in general. The influence on the values of flats in Prague basic settlement units caused by several factors such as the distance from the Old Town Square, age of dwellings, the presence of migrants or air pollution was investigated using these two methods. The price map of the association of real estate agencies, the Czech Statistical Office and the Prague Institute of Planning and Development provided the data used in the presented research. Price profiles from the centre of Prague to the suburbs in various directions were also created and analyzed. Factors with an influence on housing prices at the micro level in a case study of the Prague cadastral territory of Modřany are described in the second part of the thesis. The analysis of new developer projects and older flats in panel houses investigates the influence on the housing prices caused by factors such as noise, physical condition of apartments and the quality of...

Approved for public release; distribution is unlimited. / This paper documents the development of a statistical analysis package for the TRS-80 microcoraputer. The package is comprised of six interactive programs which are generally divided into topical areas. The major emphasis is on exploratory data analysis and statistical inference, however, probability and inverse probability distributions are also included. The programming language is TRS-80 Level II BASIC enhanced by the input/output commands available through the ESF-80 (Exatron Stringy Floppy) mass storage subsystem. With the modification of these few commands, the package is compatible with most floppy disk operating systems designed for the TRS-80 Model I or Model III microcomputers. This statistical analysis capability implemented on a relatively inexpensive system provides a useful tool to the student or the trained analyst without ready access to a mainframe computer system. / Major, United States Marine Corps

The diploma thesis describes migration and migration policy development of the Czech and Slovak republic, as well as theoretical studies dealing with migration effects on the labour market and on public finance of the migrants' destination countries. The core of the diploma thesis is an analysis of the relationship between macroeconomic indicators of the analysed countries and their migration flows, analysed by the regression analysis. The regression analysis is followed by a specific quantification of a potential impact of migration flows increase on public finance of the analysed countries. The main contribution of this diploma thesis is that it points out the connection between fiscal policy, migration flows and public finance of analysed countries. The main data sources are the OECD (2014) and EUROSTAT (2014) database and the analysed period is the year 1998 to 2012. I have come to the conclusion that there is a linear relationship between the increase of the selected macroeconomic indicators and migration flows of the analysed countries in case of the Czech and Slovak republic. This increase has negative or positive impact on their public finance, depending on type of increased macroeconomic indicator.

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This master‘s thesis deals with impacts on prices of sites which are meant by planning for residential housing in the landscape park Moravian Karst. In the research section of the thesis, there are defined fundamental concepts that concern themselves with the given issues and there are portrayed impacts on prices of sites according to the accessible literature. In the analytical section of the thesis, there is conducted an analysis of the real estate market of the given segment. Furthermore, there are examined and statistically evaluated impacts on prices of sites based on a multiple linear regression modelled in a statistical tool gretl. Impacts are evaluated both within a scope of the whole area and within particular municipalities. In the discussion section of the thesis, the manifested questions are answered, the stated hypotheses are tested and the obtained results are compared to the findings that were described in the research section.

The goal of this thesis is introduce the theory of generalized linear models, namely probit and logit model. This models are especially used for medical data processing. In our concrete case these mentioned models are applied to data file obtained in teaching hospital Brno. The aim is statically analyzed immune response of child patients in dependence of twelve selected types of genes and find out which combinations of these genes influence septic state of patients.

In the iron ore mining fraternity, in order to achieve the desired quality in the froth flotation processing plant, stakeholders rely on conventional laboratory test technique which usually takes more than two hours to ascertain the two variables of interest. Such a substantial dead time makes it difficult to put the inherent stochastic nature of the plant system in steady-state. Thus, the present study aims to evaluate the feasibility of using machine learning algorithms to predict the percentage of silica concentrate (SiO2) in the froth flotation processing plant in real-time. The predictive model has been constructed using iron ore mining froth flotation system dataset obtain from Kaggle. Different feature selection methods including Random Forest and backward elimination technique were applied to the dataset to extract significant features. The selected features were then used in Multiple Linear Regression, Random Forest and Artificial Neural Network models and the prediction accuracy of all the models have been evaluated and compared with each other. The results show that Artificial Neural Network has the ability to generalize better and predictions were off by 0.38% mean square error (mse) on average, which is significant considering that the SiO2 range from 0.77%- 5.53% -( mse 1.1%) . These results have been obtained within real-time processing of 12s in the worst case scenario on an Inter i7 hardware. The experimental results also suggest that reagents variables have the most significant influence in SiO2 prediction and less important variable is the Flotation Column.02.air.Flow. The experiments results have also indicated a promising prospect for both the Multiple Linear Regression and Random Forest models in the field of SiO2 prediction in iron ore mining froth flotation system in general. Meanwhile, this study provides management, metallurgists and operators with a better choice for SiO2 prediction in real-time per the accuracy demand as opposed to the long dead time laboratory test analysis causing incessant loss of iron ore discharged to tailings.

The aim is to investigate models that predicts the potential of pulp and evaluate the relevance of the zero-span tensile index within these. Two chemical pulps made from softwood and eucalyptus were refined in a Voith-beater with different energy input in order to study the change of fiber morphology signals and other pulp and paper properties. Chemical, THP pulp from Södra Värö is also used as an initial analysis for morphological connections to Zero-span tensile index. The L&W Fiber Tester Plus is used in order to study the pulps fiber morphology and Pulmac 2000 for zero span. Handsheets are made for mechanical tests such as tensile properties, ZD-strength and optical properties. Many of the given signals change according to clear patterns with increasing refining energy. Using least square methods, formulas describing the development with high adaptation could be formulated. Many of the measured aspects changes over already known patterns. These are then applied in the models. Three possible models is tested: linear regression, Shear-Lag and Page. Of the three, only the two first ones where able to produce reliable models, whereas the third required data that was difficult to acquire at the same time as the adaptation was very low. The only model that use exclusively morphology data is linear regression.

Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknown dynamical model of the UGV, a motion planner that plans a feasible path for the vehicle and a controller making the UGV follow the planned path. The motion planner used in the framework is a lattice-based planner based on input sampling. It uses a dynamical model of the UGV together with motion primitives, defined as a sequence of states and control signals, which are concatenated online in order to plan a feasible path between states. Furthermore, the controller that makes the vehicle follow this path is a model predictive control (MPC) controller, capable of taking the time-varying dynamics of the UGV into account as well as imposing constraints on the states and control signals. Since the dynamical model is unknown, the machine learning algorithm Bayesian linear regression (BLR) is used to continuously estimate the model parameters online during a run. The parameter estimates are then used by the MPC controller and the motion planner in order to improve the performance of the UGV. The performance of the proposed motion planning and control framework is evaluated by conducting a series of experiments in a simulation study. Two different simulation environments, containing obstacles, are used in the framework to simulate the UGV, where the performance measures considered are the deviation from the planned path, the average velocity of the UGV and the time to plan the path. The simulations are either performed with a time-invariant model, or a model where the parameters change during the run. The results show that the performance is improved when combining the motion planner and the MPC controller with the estimated model parameters from the BLR algorithm. With an improved model, the vehicle is capable of maintaining a higher average velocity, meaning that the plan can be executed faster. Furthermore, it can also track the path more precisely compared to when using a less accurate model, which is crucial in an environment with many obstacles. Finally, the use of the BLR algorithm to continuously estimate the model parameters allows the vehicle to adapt to changes in its model. This makes it possible for the UGV to stay operational in cases of, e.g., actuator malfunctions.