During the last century, the number of car users has increased as an effect of the increasing population growth. To manage the environmental and infrastructural challenges that comes with a more congested traffic network, traffic planning has become of higher importance to analyze the current traffic state and to predict future capacity challenges and effects of investments. These analysis and evaluations are commonly performed in different traffic analysis tools, where updated and realistic traffic demand needs to be provided to ensure reasonable results. In this thesis, a macroscopic model of Norrköping municipality constructed in the traffic demand modelling software Visum and a daily Origin-Destination(OD)-matrix is considered. The goal of this thesis is to produce a method that modify the current daily demand matrix into hourly demand matrices, called hourly target matrices, that represents a typical weekday. The goal is also to implement and evaluate the OD-estimation algorithm Simultaneous Perturbation Stochastic Approximation (SPSA) to obtain updated and valid demand matrices for the network model of Norrköping. The method of dividing the daily demand matrix into hourly target matrices is based on the paper by Spiess %26 Suter (1990). The method makes use of the available daily trip purpose matrices combined with hourly link flow observations from 96 links in a multiple linear regression model to obtain 24 hourly demand matrices. The resulting matrices are compared with the link flow observations and has different levels of R^2-fit, the maximum fit is 85.79 % and the minimum fit is 55.89 %. The average R^2-value is 72 %. The OD-estimation based on SPSA is performed on the AM and PM peak hours. The algorithm is implemented in Python scripts that are called from Visum where the traffic assignments is calculated. The result is an increase in R^2-value since the link flow difference between estimated and observed link flow is decreased. In total, the estimated link flows are improved by 7.4 % in the AM peak hour and 15.6 % in the PM peak hour. The total absolute change in OD-demand is 3 871 trips for AM peak hour and 6 452 trips for the PM peak hour. The estimated OD-matrices are evaluated by qualitatively visualizing the difference in heat maps and in the quantitative measure structural similarity index. The result is no major structural change from the hourly target matrices which verifies that the information used when the target matrices is produced still is considered. The total demand increased in both hours, with 505 respectively 2 431 trips and flows in some OD-pairs has a very high percental change. This was restricted by adding a penalty term to the SPSA-algorithm on the PM peak hour. The result of penalized SPSA is a much less increase of total demand as well as less percental change of the OD-flows. Though, this to a cost of not decreasing the link flow difference in the same magnitude.

L'Analyse des Données Fonctionnelles (ADF) est une branche de la statistique qui est de plus en plus utilisée dans de nombreux domaines scientifiques appliqués tels que l'expérimentation biologique, la finance, la physique, etc. Une raison à cela est l'utilisation des nouvelles technologies de collecte de données qui augmentent le nombre d'observations dans un intervalle de temps.Les jeux de données fonctionnelles sont des échantillons de réalisations de fonctions aléatoires qui sont des fonctions mesurables définies sur un espace de probabilité à valeurs dans un espace fonctionnel de dimension infinie.Parmi les nombreuses questions étudiées par l'ADF, la régression linéaire fonctionnelle est l'une des plus étudiées, aussi bien dans les applications que dans le développement méthodologique.L'objectif de cette thèse est l'étude de modèles de régression linéaire fonctionnels lorsque la covariable X et la réponse Y sont des fonctions aléatoires et les deux dépendent du temps. En particulier, nous abordons la question de l'influence de l'histoire d'une fonction aléatoire X sur la valeur actuelle d'une autre fonction aléatoire Y à un instant donné t.Pour ce faire, nous sommes surtout intéressés par trois modèles: le modèle fonctionnel de concurrence (Functional Concurrent Model: FCCM), le modèle fonctionnel de convolution (Functional Convolution Model: FCVM) et le modèle linéaire fonctionnel historique. En particulier pour le FCVM et FCCM nous avons proposé des estimateurs qui sont consistants, robustes et plus rapides à calculer par rapport à d'autres estimateurs déjà proposés dans la littérature.Notre méthode d'estimation dans le FCCM étend la méthode de régression Ridge développée dans le cas linéaire classique au cadre de données fonctionnelles. Nous avons montré la convergence en probabilité de cet estimateur, obtenu une vitesse de convergence et développé une méthode de choix optimal du paramètre de régularisation.Le FCVM permet d'étudier l'influence de l'histoire de X sur Y d'une manière simple par la convolution. Dans ce cas, nous utilisons la transformée de Fourier continue pour définir un estimateur du coefficient fonctionnel. Cet opérateur transforme le modèle de convolution en un FCCM associé dans le domaine des fréquences. La consistance et la vitesse de convergence de l'estimateur sont obtenues à partir du FCCM.Le FCVM peut être généralisé au modèle linéaire fonctionnel historique, qui est lui-même un cas particulier du modèle linéaire entièrement fonctionnel. Grâce à cela, nous avons utilisé l'estimateur de Karhunen-Loève du noyau historique. La question connexe de l'estimation de l'opérateur de covariance du bruit dans le modèle linéaire entièrement fonctionnel est également traitée. Finalement nous utilisons tous les modèles mentionnés ci-dessus pour étudier l'interaction entre le déficit de pression de vapeur (Vapour Pressure Deficit: VPD) et vitesse d'élongation foliaire (Leaf Elongation Rate: LER) courbes. Ce type de données est obtenu avec phénotypage végétal haut débit. L'étude est bien adaptée aux méthodes de l'ADF. / Functional data analysis (FDA) is a statistical branch that is increasingly being used in many applied scientific fields such as biological experimentation, finance, physics, etc. A reason for this is the use of new data collection technologies that increase the number of observations during a time interval.Functional datasets are realization samples of some random functions which are measurable functions defined on some probability space with values in an infinite dimensional functional space.There are many questions that FDA studies, among which functional linear regression is one of the most studied, both in applications and in methodological development.The objective of this thesis is the study of functional linear regression models when both the covariate X and the response Y are random functions and both of them are time-dependent. In particular we want to address the question of how the history of a random function X influences the current value of another random function Y at any given time t.In order to do this we are mainly interested in three models: the functional concurrent model (FCCM), the functional convolution model (FCVM) and the historical functional linear model. In particular for the FCVM and FCCM we have proposed estimators which are consistent, robust and which are faster to compute compared to others already proposed in the literature.Our estimation method in the FCCM extends the Ridge Regression method developed in the classical linear case to the functional data framework. We prove the probability convergence of this estimator, obtain a rate of convergence and develop an optimal selection procedure of theregularization parameter.The FCVM allows to study the influence of the history of X on Y in a simple way through the convolution. In this case we use the continuous Fourier transform operator to define an estimator of the functional coefficient. This operator transforms the convolution model into a FCCM associated in the frequency domain. The consistency and rate of convergence of the estimator are derived from the FCCM.The FCVM can be generalized to the historical functional linear model, which is itself a particular case of the fully functional linear model. Thanks to this we have used the Karhunen–Loève estimator of the historical kernel. The related question about the estimation of the covariance operator of the noise in the fully functional linear model is also treated.Finally we use all the aforementioned models to study the interaction between Vapour Pressure Deficit (VPD) and Leaf Elongation Rate (LER) curves. This kind of data is obtained with high-throughput plant phenotyping platform and is well suited to be studied with FDA methods.

The advent of the Internet and its social media platforms have affected people’s daily life. More and more people use it as a tool in order to communicate, exchange opin-ions and share information with others. However, those platforms have not only been used for socializing but also for expressing people’s product preferences. This wide spread of social networking sites has enabled companies to take advantage of them as an important way of approaching their target audience. This thesis focuses on study-ing the influence of social media platforms on the revenue of a single organization like Nike that uses them actively. Facebook and Twitter, two widely-used social me-dia platforms, were investigated with tweets and comments produced by consumer’s online discussions in brand’s hosted pages being gathered. This unstructured social media data were collected from 26 Nike official pages, 13 fan pages from each plat-form and their sentiment was analyzed. The classification of those comments had been done by using the Valence Aware Dictionary and Sentiment Reasoner (VADER), a lexicon-based approach that is implemented for social media analysis. After gathering the five-year Nike’s revenue, the degree to which these could be affected by the clas-sified data was examined by using multiple stepwise linear regression analysis. The findings showed that the fraction of positive/total for both Facebook and Twitter ex-plained 84.6% of the revenue’s variance. Fitting this data on the multiple regression model, Nike’s revenue could be forecast with a root mean square error around 287 billion.

O estudo realizado numa indústria transformadora de papéis teve por objetivo contribuir para a evidenciação da importância do monitoramento das variáveis macroeconômicas nos sistemas de informação. Existem ferramentas tecnológicas para processar esse tipo de informação e a literatura oferece suporte para a necessidade desse monitoramento, no entanto, esta prática tem sido objeto de um número limitado de trabalhos empíricos e tem recebido relativamente pouca atenção no ambiente empresarial. Uma possível justificativa, talvez seja a complexidade e o dinamismo dos dados que, além de estarem em constante oscilação per si, interagem entre si e podem ter impactos distintos em cada um dos produtos de uma mesma empresa. A pesquisa que deu origem a esta dissertação, foi realizada como um estudo exploratório descritivo, utilizando-se métodos estatísticos para processar variáveis internas e externas para analisar a importância das variáveis macroeconômicas no gerenciamento de informações de uma empresa selecionada. Escolheu-se uma só empresa com um leque diversificado de produtos e sua relação com seu ambiente externo. Na complexidade interna do negócio foram escolhidos os produtos mais representativos da empresa e cada um deles foram explorados através de duas ferramentas de análise multivariada de dados. Primeiro, fez-se uso da correlação canônica para escolher as variáveis macroeconômicas que possuíam relação mais forte com os produtos selecionados e, em seguida, utilizou-se a regressão linear múltipla para identificar as elasticidades dos produtos em relação a cada uma das variáveis macroeconômicas. Os resultados obtidos, evidentemente, não esgotam o estudo da prática apontada, mas os objetivos propostos e alcançados pela pesquisa, evidenciaram a importância do monitoramento das variáveis macroeconômicas para o caso estudado. / The study formulated in a paper transforming industry intends to contribute in the demonstration of the importance of monitoring macroeconomic variables in Information Systems. There are technological tools to process those data and the literature offers support to this need, nevertheless the subject has receive little attention in the corporate background and was the object of few empiric papers. One reason may be the complexity and the dynamism of the data which besides being in constant oscillation interact among each other and may have distinct impacts in different products of the company. The research performed in this dissertation is categorized as a descriptive exploratory study, using statistical methods to process internal and external variables in order to analyze the importance of the macroeconomic variables in the information management in a firm of choice. It was chosen a single firm with a wide range of products and its relation with the external environment. In the internal complexity of the business it was chosen the most representative products and each one of them were analyzed trough two different multivariate data analysis. First the Canonic Correlation in order to select the macroeconomic variables with the strongest relation to the selected products, subsequently the Multiple Linear Regression with the intention of pinpointing the elasticity of the same products to each variable. The results clearly do not exhaust the subject, however corroborate with the proposed and reached objectives highlighting the importance of monitoring macroeconomic variables for the case studied.

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No. of bitstreams: 1 Dissertacao - Sergio Rodrigues - Final.pdf: 12749163 bytes, checksum: 58e8b709bc8e635d832d81c9b158aba6 (MD5) Previous issue date: 2015-07-17 / A escolha da cidade do Rio de Janeiro como sede de grandes eventos esportivos mundiais, a Copa do Mundo de Futebol de 2014 e os Jogos Olímpicos de 2016, colocou-a no centro de investimentos em infraestrutura, mobilidade urbana e segurança pública, com consequente impacto no mercado imobiliário, tanto de novos lançamentos de empreendimentos, quanto na revenda de imóveis usados. Acredita-se que o preço de um imóvel dependa de uma relação entre suas características estruturais como quantidade de quartos, suítes, vagas de garagem, presença de varanda, tal como sua localização, proximidade com centros de trabalho, entretenimento e áreas valorizadas ou degradadas. Uma das técnicas para avaliar a contribuição dessas características para a formação do preço do imóvel, conhecido na Econométrica como Modelagem Hedônica de Preços, é uma aplicação de regressão linear multivariada onde a variável dependente é o preço e as variáveis independentes, as respectivas características que deseja-se modelar. A utilização da regressão linear implica em observar premissas que devem ser atendidas para a confiabilidade dos resultados a serem analisados, tais como independência e homoscedasticidade dos resíduos e não colinearidade entre as variáveis independentes. O presente trabalho objetiva aplicar a modelagem hedônica de preços para imóveis localizados na cidade do Rio de Janeiro em um modelo de regressão linear multivariada, em conjunto com outras fontes de dados para a construção de variáveis de acessibilidade e socioambiental a fim de verificar a relação de importância entre elas para a formação do preço e, em particular, exploramos brevemente a tendência de preços em função da distância a favelas. Em atenção aos pré-requisitos observados para a aplicação de regressão linear, verificamos que a premissa de independência dos preços não pode ser atestada devido a constatação da autocorrelação espacial entre os imóveis, onde não apenas as características estruturais e de acessibilidade são levadas em consideração para a precificação do bem, mas principalmente a influência mútua que os imóveis vizinhos exercem um ao outro. / The choice of the city of Rio de Janeiro as the host of major world sporting events, the Football World Cup 2014 and the Olympic Games in 2016, put it in the center of investments in infrastructure, urban mobility and public safety, with consequent impact on the real estate market, new enterprise releases, as in resale of used real estate units. It is believed that the price of a real estate unit depends on a relationship between their structural characteristics like the number of rooms, suites, parking spaces, balcony presence, such as its location, proximity to work centers, entertainment and reclaimed or degraded areas. One of the techniques to assess the contribution of these features to the formation of the property price, known as the Econometric Modeling Hedonic Price, is an application of multivariate linear regression where the dependent variable is the price and the independent variables, the respective characteristics desired to model. The use of linear regression implies observe the assumptions that must be met for the reliability of the results to be analyzed, such as independence and homoscedasticity of the residuals and no collinearity among the independent variables. This paper aims to apply the hedonic modelling prices for properties located in the city of Rio de Janeiro in a multivariate linear regression model, together with other data sources for building accessibility and social environmental variables in order to verify the relationship of importance among them for the prices and, in particular, briefly explore the role-price trend when moving away from the slums. Noting the prerequisites observed for the application of linear regression, we have found that the assumption of independence of prices cannot be proved due to the presence of spatial autocorrelation between the real estate units, where not only the structural and accessibility features are taken into account for the pricing process, but mostly the mutual influence neighbouring properties carry each other.

O estudo realizado numa indústria transformadora de papéis teve por objetivo contribuir para a evidenciação da importância do monitoramento das variáveis macroeconômicas nos sistemas de informação. Existem ferramentas tecnológicas para processar esse tipo de informação e a literatura oferece suporte para a necessidade desse monitoramento, no entanto, esta prática tem sido objeto de um número limitado de trabalhos empíricos e tem recebido relativamente pouca atenção no ambiente empresarial. Uma possível justificativa, talvez seja a complexidade e o dinamismo dos dados que, além de estarem em constante oscilação per si, interagem entre si e podem ter impactos distintos em cada um dos produtos de uma mesma empresa. A pesquisa que deu origem a esta dissertação, foi realizada como um estudo exploratório descritivo, utilizando-se métodos estatísticos para processar variáveis internas e externas para analisar a importância das variáveis macroeconômicas no gerenciamento de informações de uma empresa selecionada. Escolheu-se uma só empresa com um leque diversificado de produtos e sua relação com seu ambiente externo. Na complexidade interna do negócio foram escolhidos os produtos mais representativos da empresa e cada um deles foram explorados através de duas ferramentas de análise multivariada de dados. Primeiro, fez-se uso da correlação canônica para escolher as variáveis macroeconômicas que possuíam relação mais forte com os produtos selecionados e, em seguida, utilizou-se a regressão linear múltipla para identificar as elasticidades dos produtos em relação a cada uma das variáveis macroeconômicas. Os resultados obtidos, evidentemente, não esgotam o estudo da prática apontada, mas os objetivos propostos e alcançados pela pesquisa, evidenciaram a importância do monitoramento das variáveis macroeconômicas para o caso estudado. / The study formulated in a paper transforming industry intends to contribute in the demonstration of the importance of monitoring macroeconomic variables in Information Systems. There are technological tools to process those data and the literature offers support to this need, nevertheless the subject has receive little attention in the corporate background and was the object of few empiric papers. One reason may be the complexity and the dynamism of the data which besides being in constant oscillation interact among each other and may have distinct impacts in different products of the company. The research performed in this dissertation is categorized as a descriptive exploratory study, using statistical methods to process internal and external variables in order to analyze the importance of the macroeconomic variables in the information management in a firm of choice. It was chosen a single firm with a wide range of products and its relation with the external environment. In the internal complexity of the business it was chosen the most representative products and each one of them were analyzed trough two different multivariate data analysis. First the Canonic Correlation in order to select the macroeconomic variables with the strongest relation to the selected products, subsequently the Multiple Linear Regression with the intention of pinpointing the elasticity of the same products to each variable. The results clearly do not exhaust the subject, however corroborate with the proposed and reached objectives highlighting the importance of monitoring macroeconomic variables for the case studied.

A emergência frequente de oscilações de corrente e potencial durante a eletro-oxidação de moléculas orgânicas pequenas tem implicações mecanísticas importantes, como por exemplo, na conversão reacional global e, portanto, no desempenho de dispositivos práticos de conversão de energia. Orientado nesse sentido, este trabalho desenvolveu-se por meio de duas frentes relacionadas: (a) utilizando-se medidas obtidas por meio do acoplamento de uma célula eletroquímica a um espectrômetro de massas, estudou-se a dinâmica da produção de espécies voláteis durante a eletro-oxidação oscilatória de ácido fórmico, metanol e etanol. Além da apresentação de resultados experimentais ainda não relatados, introduz-se o uso de regressão linear multivariada para se comparar a corrente faradaica total estimada, com a proveniente da produção de espécies voláteis detectáveis: dióxido de carbono para ácido fórmico, dióxido de carbono e metilformiato para metanol e, dióxido de carbono e acetaldeído para etanol. A análise fornece a melhor combinação das correntes iônicas detectadas para se representar a corrente global ou a máxima contribuição faradaica possível devido à produção de espécies voláteis. Os resultados foram discutidos em conexão com aspectos do mecanismo reacional de cada molécula. A incompatibilidade entre a corrente faradaica total estimada e a obtida pela melhor combinação das correntes parciais provenientes da produção de espécies voláteis foi pequena para ácido fórmico, quatro e cinco vezes maior para etanol e metanol, respectivamente, evidenciando, nestes dois últimos casos, o aumento do papel desempenhado por espécies solúveis parcialmente oxidadas; (b) investigou-se características gerais da eletro-oxidação de formaldeído, ácido fórmico e metanol sobre platina em meio ácido, com ênfase na comparação do desempenho eletrocatalítico global sob condições estacionária e oscilatória. A comparação procedeu-se por meio da interpretação de resultados tratados de diferentes formas e generalizada pela utilização das mesmas condições experimentais em todos os casos. Para todos os sistemas, o baixo potencial alcançado durante as oscilações evidenciou uma considerável diminuição do sobrepotencial associado à reação anódica, se comparado com o obtido na ausência de oscilações. Além do mais, o processo de reativação superficial do catalisador que ocorre durante as oscilações amplia o desempenho de todos os sistemas em termos de atividade eletrocatalítica. Por fim, também são discutidos alguns aspectos do mecanismo reacional das moléculas estudadas. / The frequent emergence of current/potential oscillations during the electrooxidation of small organic molecules has implications on mechanistic aspects such as, for example, on the overall reaction conversion, and thus on the performance of practical devices of energy conversion. In this direction, this work is divided in two parts: (a) by means of on line Differential Electrochemical Mass Spectrometry (DEMS) it was studied the production of volatile species during the electrooxidation of formic acid, methanol and ethanol. Besides the presentation of previously unreported DEMS results on the oscillatory dynamics of such systems, it was introduced the use of multivariate linear regression to compare the estimated total faradaic current with the one comprising the production of volatile detectable species, namely: carbon dioxide for formic acid, carbon dioxide and methylformate for methanol and, carbon dioxide and acetaldehyde for ethanol. The introduced analysis provided the best combination of the DEMS ion currents to represent the total faradaic current or the maximum possible faradaic contribution of the volatile products for the global current. The results were discussed in connection with mechanistic aspects for each system. The mismatch between estimated total current and the one obtained by the best combination of partial currents of volatile products was found to be small for formic acid, 4 and 5 times bigger for ethanol and methanol, respectively, evidencing the increasing role played by partially oxidized soluble species in each case; (b) it was investigated general features of the electro-oxidation of formaldehyde, formic acid and methanol on platinum and in acid media, with emphasis on the comparison of the performance under stationary and oscillatory regimes. The comparison is carried out by different means and generalized by the use of identical experimental conditions in all cases. In all three systems studied, the occurrence of potential oscillations is associated with excursions of the electrode potentials to lower values, which considerable decreases the overpotential of the anodic reaction, when compared to that in the absence of oscillations. In addition, the reactivation of catalyst surface benefits the performance of all systems in terms of electrocatalytic activity. Finally, some mechanistic aspects of the studied reactions are also discussed.

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No. of bitstreams: 1 silvajunior_gc_me_rcla.pdf: 1724008 bytes, checksum: c953406384f7a6ee2d78df6e9f2ae70b (MD5) Previous issue date: 2017-10-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, apresentamos um relato de experimento realizado junto aos alunos de ensino fundamental de uma escola pública municipal e efetuamos o ajuste de curva dos dados observados por meio do método dos mínimos quadrados. Para tanto, discutimos a concepção e aplicação desse método a partir de resultados oriundos do cálculo diferencial, da álgebra linear e alguns conceitos estatísticos. Do cálculo diferencial estudamos a minimização dos erros de aproximação por meio da investigação dos pontos de mínimo da função erro. Da álgebra linear determinamos os parâmetros da função ajustada através da discussão e solução de um sistema de equações lineares resultante do conjunto de derivadas parciais nulas que estabelecem o ponto crítico da função erro. Da estatística utilizamos alguns conceitos e formulações que tratam da intensidade da relação entre as variáveis, bem como, das incertezas na variável dependente e nos parâmetros da função ajustada. / In this work, we present a report of an experiment carried out with elementary school students of a municipal public school, and we performed the curve adjustment of the observed data through the least squares method. For this, we discuss the conception and application of this method from results derived from differential calculus, linear algebra and some statistical concepts. From the differential calculation, we study the minimization of approximation errors by investigating the minimum points of the error function. From linear algebra, we determine the parameters of the adjusted function through the discussion and solution of a system of linear equations resulting from the set of null partial derivatives that establish the critical point of the error function. From statistics, we use some concepts and formulations that deal with the intensity of the relationship between variables, as well as the uncertainties in the dependent variable and the parameters of the adjusted function.

Submitted by Valquíria Barbieri (kikibarbi@hotmail.com) on 2018-04-30T22:39:20Z No. of bitstreams: 1 DISS_2015_Ariel Souza Rossi.pdf: 2445630 bytes, checksum: 83e29452e738e684605b37aae31b6bb3 (MD5) / Approved for entry into archive by Jordan (jordanbiblio@gmail.com) on 2018-05-16T14:56:01Z (GMT) No. of bitstreams: 1 DISS_2015_Ariel Souza Rossi.pdf: 2445630 bytes, checksum: 83e29452e738e684605b37aae31b6bb3 (MD5) / Made available in DSpace on 2018-05-16T14:56:01Z (GMT). No. of bitstreams: 1 DISS_2015_Ariel Souza Rossi.pdf: 2445630 bytes, checksum: 83e29452e738e684605b37aae31b6bb3 (MD5) Previous issue date: 2015-06-19 / O objetivo desta pesquisa consistiu em ajustar um modelo de fator de forma e modelo de afilamento para Pinus caribaea var. caribaea Barr. et Golf. para a região Centro-Sul de Mato Grosso. O povoamento localizado no município de Nova Marilândia possui área de 215,47ha, e o seu entorno é ocupado por atividades agrícolas. A implantação foi efetuada com mudas de origem seminal e o povoamento não recebeu qualquer tratamento silvicultural e os dados foram coletados em 2014, quando o povoamento possuía 36 anos. A descrição do povoamento ocorreu através de um inventário aleatório, visando representar todo o povoamento. Posteriormente foi realizada a classificação sociológica do povoamento de acordo com a metodologia de Hozokawa e Souza. Os dados coletados são provenientes de 90 árvores, oriundas de todas as classes sociológicas. Para o ajuste do fator de forma foram testados 10 modelos e para o ajuste do afilamento foram testados os modelos de Kozak, Polinômio de Potências Fracionárias e Hradetzky. Além disso, foram comparados os volumes obtidos através do fator de forma e através da integral da função de afilamento com o volume obtido através da cubagem rigorosa. Dentre os modelos de fator de forma avaliados, o que melhor ajustou aos dados foi um modelo logarítmico; o modelo de Hradetzky apresentou melhor ajuste ao afilamento e calculou mais precisamente o volume total. A comparação dos volumes estimados e o calculado, ao nível de 5% (α = 0,05) para o teste de Tukey não foi verificada diferença estatisticamente significativa. / The aim of this research consisted adjust a model of factor of form and model of afilamento caribaea Pinus to var. caribaea Barr. et Golf. for the region Center-South of Mato Grosso. The population located in the city of Nova Marilândia possesses area of 215,47ha, and around is occupied for agricultural activities. The implantation was performed with seedlings of seminal origin, the population did not receive any silvicultural treatment and the data were collected in 2014, when the population had 36 years. The description of the population occurred through a random inventory, aiming at to represent the population all. Subsequently the sociological classification of the population was carried in accordance with the methodology of Hozokawa and Souza. The collected data are proceeding from 90 trees, deriving of all the sociological classes. For the adjustment of the form factor 10 models were tested and for the adjustment of the tapering, the models of Kozak, Polynomial of Powers Fracionárias and Hradetzky, were tested. Moreover, the volumes obtained through the factor of form and the integral of the tapper function were compared with the volume obtained through the rigorous cubage. Amongst the models of form factor evaluated, what better it adjusted to the data was a logarithmic model; the model of Hradetzky presented adjustment to the tapering better and calculated the total volume more precisely. The comparison of the estimated volumes and the calculated one, to the level of 5% (α = 0,05) for the test of Tukey was not verified statistically significant difference.

abstract: Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand. / Dissertation/Thesis / M.S. Computer Science 2013