Academic literature on the topic 'Multiple Regression Estimate'
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Journal articles on the topic "Multiple Regression Estimate"
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ALVES, M. E., and A. LAVORENTI. "Remaining Phosphorus Estimate Through Multiple Regression Analysis." Pedosphere 16, no. 5 (October 2006): 566–71. http://dx.doi.org/10.1016/s1002-0160(06)60089-1.
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Li, Zhong-xiao. "Adaptive multiple subtraction based on support vector regression." GEOPHYSICS 85, no. 1 (November 22, 2019): V57—V69. http://dx.doi.org/10.1190/geo2018-0245.1.
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ABSTRACT Multiple removal is essential for seismic imaging in marine seismic processing. After prediction of multiple models, adaptive multiple subtraction is an important procedure for multiple removal. Generally, adaptive multiple subtraction can be conducted by the iterative reweighted least-squares (IRLS) algorithm with an [Formula: see text]-norm minimization constraint of primaries. We have developed a machine-learning algorithm into adaptive multiple subtraction, which is implemented based on support vector regression (SVR). Our SVR-based method contains training and prediction stages. During the training stage, an SVR function is estimated by solving a dual optimization problem with the feature vectors of the predicted multiples and the target values of the original data. The SVR function can transform predicted multiples nonlinearly for a better match between the predicted multiples and the true multiples. Furthermore, we use the SVR function to estimate multiples in the prediction stage by inputting the feature vectors of predicted multiples. Then, multiple-removal results are obtained by subtracting the estimated multiples directly from the original data. Synthetic and field data examples demonstrate that our SVR-based method can better balance multiple removal and primary preservation than the IRLS-based method.
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Gorgees, HazimMansoor, and FatimahAssim Mahdi. "Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients." Journal of Physics: Conference Series 1003 (May 2018): 012049. http://dx.doi.org/10.1088/1742-6596/1003/1/012049.
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Rahayu, Elvri, Iin Parlina, and Zulia Almaida Siregar. "Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation." JOMLAI: Journal of Machine Learning and Artificial Intelligence 1, no. 1 (March 18, 2022): 1–10. http://dx.doi.org/10.55123/jomlai.v1i1.142.
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CV. Kerinci Motor is a company engaged in the transportation and automotive sector, especially in the sale of motorcycles. The uncertainty in the number of motorcycle sales at this company has hampered the company's development, because the company cannot take definite policies regarding the sales that occur. Therefore, it is necessary to estimate the sales of motorcycles at this company in the future, so that the management can estimate consumer demand in the future. So that the company is able to serve and provide consumer demand. The estimation algorithm that will be used in this research is Multiple Linear Regression which is one of the data mining methods. This method was chosen because it is able to make an estimate by utilizing data regarding sales. The results of the estimated (estimated) sales of manual motorcycles in 2021 by January are 56,941 or 57 motorcycles in the manual category. This means that there is an increase in the number of manual motorbikes by 5 motorbikes, while the results until May 2021 amounted to 65,710 motorbikes. So it can be concluded that sales of motorcycles at CV. Kerinci Motor have increased sales in the next 5 months.
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Suparta, W., and W. S. Putro. "Using multiple linear regression model to estimate thunderstorm activity." IOP Conference Series: Materials Science and Engineering 185 (March 2017): 012023. http://dx.doi.org/10.1088/1757-899x/185/1/012023.
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Long, Michael A., Kenneth J. Berry, and Paul W. Mielke. "A Note on Permutation Tests of Significance for Multiple Regression Coefficients." Psychological Reports 100, no. 2 (April 2007): 339–45. http://dx.doi.org/10.2466/pr0.100.2.339-345.
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In the vast majority of psychological research utilizing multiple regression analysis, asymptotic probability values are reported. This paper demonstrates that asymptotic estimates of standard errors provided by multiple regression are not always accurate. A resampling permutation procedure is used to estimate the standard errors. In some cases the results differ substantially from the traditional least squares regression estimates.
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Ellington, E. Hance, Guillaume Bastille‐Rousseau, Cayla Austin, Kristen N. Landolt, Bruce A. Pond, Erin E. Rees, Nicholas Robar, and Dennis L. Murray. "Using multiple imputation to estimate missing data in meta‐regression." Methods in Ecology and Evolution 6, no. 2 (December 17, 2014): 153–63. http://dx.doi.org/10.1111/2041-210x.12322.
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White, Edward D., Vincent P. Sipple, and Michael A. Greiner. "Using Logistic and Multiple Regression to Estimate Engineering Cost Risk." Journal of Cost Analysis & Management 6, no. 1 (July 2004): 67–79. http://dx.doi.org/10.1080/15411656.2004.10462248.
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Shen, Gang. "Asymptotics of a Theil-type estimate in multiple linear regression." Statistics & Probability Letters 79, no. 8 (April 2009): 1053–64. http://dx.doi.org/10.1016/j.spl.2008.12.017.
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Chen, Xiru, and Hongzhi An. "Abnormal behavior of the least squares estimate of multiple regression." Science in China Series A: Mathematics 40, no. 3 (March 1997): 234–42. http://dx.doi.org/10.1007/bf02874515.
Full textDissertations / Theses on the topic "Multiple Regression Estimate"
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Kerr, Barry Douglas. "Multiple Regression Equations to Estimate Mean Nutrient Concentrations in Streams of North Central Texas from Landsat Derived Land Use." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278778/.
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Nutrients are of critical concern in water quality assessment. The development of empirical models to estimate mean nutrient concentrations, based on satellite derived land use, could aid water resource managers. Models using land use acreages outperformed those using percentages, and discrete urban land uses were superior to lumped urban. Regressions of the combinations of two, three and four of the eight possible land use variables were investigated. Sensitivity analyses, with one stream deleted each series, identified robust combinations of variables at each level. Although uncertainty exists regarding the final regression coefficients, five of the six actual measured nitrate and total phosphorus mean concentrations were within the 95 percent confidence limits.
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Ernstsson, Hampus, and Liljesvan Max Börjes. "Multiples for Valuation Estimates of Life Science Companies in Sweden." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254239.
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Market multiples are a common and simple tool for estimation of corporate value. It can express temporal dynamics and differences in markets, industries and firms. Despite their practical usefulness, some critical problems remains which continue to be debated. This thesis investigates if there exists characteristics for explaining market capitalization by market multiples within the life science industry in Sweden. The approach follows well known theory of multiple linear regression analysis. The results indicated only a linear relationship between the market cap and the R\&D expenditures of a company. This does not mean that the other explanatory variables does not have effect on market cap only that there is no linear relationship that could be statistically proven.Värderingsmultiplar är ett vanligt och enkelt verktyg för att approximera företags värde. Det kan beskriva temporär dynamik och skillnader hos marknader, industrier och bolag. Trots dess praktiska användbarhet finns en del kritiska problem som fortfarande debatteras. Denna uppsats undersöker om det existerar några egenskaper för att förklara marknadsvärdet med hjälp av värderingsmultiplar inom life science industrin i Sverige. Tillvägagångssättet följer välkänd teori om multipel linjär regressions analys. Resultaten visade att det endast finns ett samband mellan marknadsvärdet och utgifter för forskning och utveckling för ett bolag. Detta innebär inte att de andra variablerna inte har någon effekt på marknadsvärdet, utan att det inte finns ett linjärt samband som kan bevisas på ett statistiskt vis.
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Jastram, John Dietrich. "Improving Turbidity-Based Estimates of Suspended Sediment Concentrations and Loads." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/32514.
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As the impacts of human activities increase sediment transport by aquatic systems the need to accurately quantify this transport becomes paramount. Turbidity is recognized as an effective tool for monitoring suspended sediments in aquatic systems, and with recent technological advances turbidity can be measured in-situ remotely, continuously, and at much finer temporal scales than was previously possible. Although turbidity provides an improved method for estimation of suspended-sediment concentration (SSC), compared to traditional discharge-based methods, there is still significant variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. The purpose of this study was to improve the turbidity-based estimation of SSC. Working at two monitoring sites on the Roanoke River in southwestern Virginia, stage, turbidity, and other water-quality parameters and were monitored with in-situ instrumentation, suspended sediments were sampled manually during elevated turbidity events; those samples were analyzed for SSC and for physical properties; rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC-estimation variance and hydrologic variables that contribute to variance in those physical properties. Results indicated that the inclusion of any of the measured physical properties, which included grain-size distributions, specific surface-area, and organic carbon, in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables, which were measured remotely and on the same temporal scale as turbidity, to represent these physical properties, resulted in a model which was equally as capable of predicting SSC. A square-root transformed turbidity-based SSC estimation model developed for the Roanoke River at Route 117 monitoring station, which included a water level variable, provided 63% less unexplained variance in SSC estimations and 50% narrower 95% prediction intervals for an annual loading estimate, when compared to a simple linear regression using a logarithmic transformation of the response and regressor (turbidity). Unexplained variance and prediction interval width were also reduced using this approach at a second monitoring site, Roanoke River at Thirteenth Street Bridge; the log-based transformation of SSC and regressors was found to be most appropriate at this monitoring station. Furthermore, this study demonstrated the potential for a single model, generated from a pooled set of data from the two monitoring sites, to estimate SSC with less variance than a model generated only from data collected at this single site. When applied at suitable locations, the use of this pooled model approach could provide many benefits to monitoring programs, such as developing SSC-estimation models for multiple sites which individually do not have enough data to generate a robust model or extending the model to monitoring sites between those for which the model was developed and significantly reducing sampling costs for intensive monitoring programs.Master of Science
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Garcia, Altemir Tomaz de Carvalho. "Estimativa de demanda de energia elétrica em uma instituição de ensino superior." Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/8150.
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In recent years, several studies where published regarding to the estimation of variables related to the use of electricity, where the most varied methodologies are used to perform modeling and estimation of demand for energy of countries, States, companies in general and educational systems. In this dissertation where chosen this last category and the focus is on Higher Education Institutions (HEIs). Looking for drawing up an estimate of Wing Maxim Demand (WMD), monthly of electrical energy power, for the (HEIs), from the amount of students and, if necessary, from other causal variables, which can contribute to managerial way for the renegotiation of contracts with concessionaires that lead to annual cost savings and still contribute to a better control of the levels of maxim demand of electricity. To achieve this objective, it was realized a review of the literature regarding to the variables that could introduce correlation with the dependent variable WMD. This review indicated several methodologies that could contribute to the solution of the problem proposed: Markov Chain, Support vector Regression methodology, Genetic Programming Model and Artificial Neural Networks. It was adopted the methodology of Multiple Linear Regression (MLR) because it is less complex and a methodology directed at large companies. It was selected an IES and were carried out interviews with some engineers and technician of his electrical engineering division, seeking to better understand energy use and the behavior of the variable WMD in this IES being made available the reports of power energy monitoring where the WMD data of January-December 2008 of 2014 were contained. So on the basis of these data and documental research of the independent variables, and, through the methodologies of Multiple Linear Regression (MLR), it was developed a model from the data of 72 months which had their waste evaluated, showing a coefficient of determination R ^ 2 equal to 0.883. Independent variables that remained in the model, from the use of the backward method, were 4 (four) Dummy variables associated with the years, six variables of this type associated with the months and a variable which is the product of school days for graduates and the quantity of graduate students registered. This model was able to identify seasonality presents in the behavior of the WMD of this HIE. It would allow the hiring of WMD per month, that would make savings of 57% compared to the traditional contracting mode (WMD fixed for the entire period), considering the period from July to December, before the period left for validation. In conclusion, a forecast for the period of January to May 2015 and the adoption of the proposed model was able to provide a savings of 45% in relation to the scheme currently used by this HEI.
Nos últimos anos, diversos trabalhos foram publicados em relação à estimativa de variáveis relacionadas ao uso da energia elétrica, onde as mais variadas metodologias são utilizadas para realizar a modelagem e estimação da demanda por energia de países, Estados, empresas em geral e dos sistemas de ensino. Nesta dissertação foi escolhida esta última categoria e o foco consiste nas Instituições de Ensino Superior (IES). Procurando elaborar uma estimativa de Demanda Máxima de Ponta (DMP), mensal de potência de energia elétrica adequada às IESS, a partir da quantidade de alunos, e, se necessário, a partir de outras variáveis causais, que possam contribuir de maneira gerencial para a renegociação de contratos com concessionárias que levem à redução de custos anuais e que ainda podem contribuir para um melhor controle dos níveis de demanda máxima de energia elétrica. Para alcançar tal objetivo, foi realizada uma revisão da literatura a respeito de variáveis que poderiam apresentar correlação com a variável dependente DMP. Esta revisão indicou várias metodologias que poderiam contribuir para a solução do problema proposto: a Cadeia de Markov, a Metodologia de Regressão do vetor de Suporte, o Modelo de Programação Genética e as Redes Neurais Artificiais. Por ser uma metodologia menos complexa e direcionada a empresas de grande porte, adotou-se a Metodologia de Regressão Linear Múltipla (RLM). Foi selecionada uma IES e foram realizadas entrevistas com alguns engenheiros e técnico da sua divisão de engenharia elétrica, procurando entender melhor o uso da energia e o comportamento da variável DMP nesta IES, sendo disponibilizados os relatórios de energia do sistema de monitoração de energia onde os dados de DMP de janeiro de 2008 a dezembro de 2014 estavam contidos. Então, com base nestes dados e em pesquisa documental das candidatas a variáveis independentes, e, através da Metodologia (RLM), foi desenvolvido um modelo a partir dos dados de 72 meses, que teve seus resíduos avaliados, apresentando um coeficiente de determinação 𝑅2 igual a 0,883 .As variáveis independentes que permaneceram no modelo, a partir da utilização do método backward, foram 4(quatro) variáveis Dummy associadas a anos, seis variáveis deste tipo associadas a meses e uma variável fruto do produto entre dias letivos de graduação e quantidade de alunos da graduação matriculados. O modelo foi capaz de identificar a sazonalidade presente no comportamento da DMP da IES em estudo. Ele possibilitaria a contratação de DMP por mês, o que daria uma economia de 57% em relação ao modo de contratação tradicional (DMP fixo para todo o período), considerando o período de julho a dezembro, antes do período deixado para validação. Concluindo, foi realizada uma previsão para o período de janeiro a maio de 2015 e a adoção do modelo proposto foi capaz de proporcionar uma economia de 45% em relação ao esquema utilizado atualmente pela IES.
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Mabilana, Hugo Adriano. "Desenvolvimento de modelo agrometeorológico espectral para estimativa de rendimento do milho na província de Manica-Moçambique." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/30188.
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A república de Moçambique é um país localizado ao longo da costa Leste da África Austral, com a economia baseada essencialmente na prática da agricultura. A cultura do milho (Zea mays L.) é a mais importante, cultivada em regime de sequeiro, com rendimentos dependentes das condições meteorológicas. Modelos agrometeorológicos de estimativa de rendimentos de culturas alimentares são alternativas viáveis para tomada de decisão em medidas de segurança alimentar e abastecimento. O calendário agrícola e o sistema de produção tornam o uso de geotecnologias uma importante ferramenta para o monitoramento de culturas e o desenvolvimento de modelos de estimativa de rendimentos. Produtos de dados de sensoriamento remoto, como índices espectrais combinados com parâmetros agrometeorológicos podem melhorar as representações espaciais de rendimentos do milho em Moçambique. O ajuste de um modelo agrometeorológico espectral para estimativa de rendimentos do milho por regressão linear múltipla na província de Manica-Moçambique constituiu o objetivo do estudo. Foi realizado um mapeamento de áreas agrícolas por análise multitemporal do NDVI/MODIS e também foi avaliada a eficiência de variáveis agrometeorológicas e espectrais na estimativa de rendimentos do milho em uma área da província de Manica que envolve os distritos de Gondola, Manica, Mossurize e Sussundenga, responsáveis por mais de 80% da produção de milho na província nos anos de 2000 a 2009. Foi desenvolvido um modelo de início do ciclo do milho baseado em critérios de chuva, e estabelecendo um ciclo fixo do milho em 130 dias. A metodologia de mapeamento de áreas agrícolas consistiu em somatórios de imagens binárias geradas por diferença de NDVI máximo e mínimo ao longo do ciclo e estabelecimento de níveis de restrição com base em comparações com estatísticas oficiais por distrito. As variáveis agrometeorológicas testadas foram evapotranspiração relativa (ETr/ETm) e o índice de satisfação das necessidade de água (ISNA) calculados a partir de dados de estimativas de elementos meteorológicos do modelo do ECMWF. O conjunto de variáveis espectrais compreendiam composições de 16 dias de índices de vegetação EVI e NDVI provenientes do produto MOD13Q1 do sensor MODIS e o LSWI, gerado por diferença normalizada de bandas de refletância de superfície do infravermelho próximo e médio contidas no mesmo produto. O modelo agrometeorológico espectral envolveu as variáveis meteorológicas e espectrais como independentes sendo o rendimento médio e relativo, as variáveis dependentes ajustadas em um modelo de regressão múltipla. Todos os distritos, a exceção de Mossurize, geraram modelos com bom desempenho nas estimativas de rendimentos do milho e significado físico. O modelo regional, incluindo Gondola, Manica e Sussundenga e envolvendo o rendimento relativo foi o mais recomendado para estimativa de rendimentos do milho na região com r2 = 0,762 e RMSE de 9,46%.Mozambique is a country located along the east coast of southern Africa, with an economy based primarily on agriculture. The Maize crop (Zea mays L.) is the most important crop, growing in rainfed conditions, with its yield dependent only on weather conditions. Agrometeorological models to forecast yields of food crops are viable alternatives for decision making on food safety measures and supply. The agricultural calendar and the production system make use of geotechnologies an important tool for crop monitoring and yield forecasting. Products from remote sensing data, combined with spectral indices and agrometeorological parameters can improve the spatial representations of maize yields in Mozambique. Setting an agrometeorological model to estimate the spectral yield of corn by multiple linear regression in Manica province, Mozambique was the objective of the study. Were conducted a mapping of agricultural areas by analyzing multitemporal NDVI / MODIS and also evaluated the effectiveness of spectral and meteorological variables in the estimated maize yield in an area of Manica province involving the districts of Gondola, Manica, Mossurize and Sussundenga responsible for more than 80% of corn production in the province in the years 2000 to 2009. A model was developed to estimate the beginnig of the corn cycle, using as a criteria the rainfall, and setting a fixed cycle of corn in 130 days. The methodology for mapping agricultural areas consisted of sums of binary images generated by the difference of maximum and minimum NDVI throughout the cycle and establishing levels of restriction based on comparisons with official statistics by district. Were tested the meteorological variables: the relative evapotranspiration (ETr / ETm) and the index of satisfaction of water needs (ISNA) calculated from data from meteorological model of ECMWF. The set of spectral variable were comprised of 16 days composition of vegetation indices NDVI and EVI from the MODIS product MOD13Q1 and LSWI generated from normalized difference of surface reflectance bands of near-infrared and medium infrared contained the same product. The meteorological and spectral variables was the set of independent variables and the average and relative yield were the set of dependent variables used to adjusted a multiple regression model, called agrometeorological-spectral model. To all districts, except for Mossurize were generated models with good performance in estimating the corn yield and with physical meaning. The regional model, including Gondola, Manica and Sussundenga and involving the relative yield was the most suitable for estimating corn yield in the region with r2 = 0.762 and RMSE of 9.46%.
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Coudret, Raphaël. "Stochastic modelling using large data sets : applications in ecology and genetics." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00865867.
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There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.
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Vargas, Luciano Lopes Amor. "Modelo para estimar a geração de resíduo de madeira de uso provisório em obras de edifícios verticai." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6311.
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O setor da construção civil é hoje um grande gerador de resíduos, o que impacta negativamente o meio ambiente. Tanto a fase de projeto quanto a de execução de obras podem ocasionar essa geração. Quantificar o resíduo gerado em uma obra é fundamental para ter um controle do problema. A madeira, principal material empregado no uso provisório nos canteiros de obras, pode gerar grande volume de resíduo, sendo que o mesmo é descartado tão logo a obra termina, havendo pouco reaproveitamento ou reciclagem da mesma. Quantificar esse resíduo pode facilitar a gestão e o controle do mesmo, possibilitando a sustentabilidade de um material nobre e importante para o desenvolvimento da construção civil. O objetivo deste estudo consiste em propor um modelo para estimar a geração de resíduos de madeira de uso provisório em obras de edifícios verticais, analisando suas características de projeto e de execução. A estratégia de pesquisa utilizada foi o estudo de casos múltiplos, com coleta de dados de obras localizadas na região metropolitana de Porto Alegre, feitos através de entrevistas, observações in loco e análise de projetos. Ao todo, a amostra estudada foi formada por 22 obras de 11 empresas. Como resultado, foram gerados modelos estatísticos, a partir de regressão múltipla, contendo a variável dependente - volume de resíduos de madeira – e as variáveis independentes relativas ao projeto – número de pavimentos, número de pavimentos em subsolo, volume de concreto executado – e à execução - comprimento linear de tapume em madeira, índice de uso da madeira na confecção dos equipamentos de proteção coletiva e instalações provisórias. O modelo proposto possui R² ajustado de 0,893, indicando que é capaz de explicar 89,3% do fenômeno. As variáveis que mais impactaram foram o número de pavimentos e o número de pavimentos em subsolo. O modelo permite que as empresas construtoras possuam maior controle sobre o volume de resíduo de madeira gerado pelas suas obras, e possibilita realizar ações efetivas em prol da gestão dos resíduos, havendo maior controle e a redução dos mesmos. Possibilita também aplicá-lo em outras obras de diferentes regiões, considerando as características de projeto e de execução locais.
The construction industry is today a great generator of waste, which has a negative impact on the environment. Both the design and execution phases of works can cause this generation. To quantify the waste generated in a work is fundamental to control the problem. Wood, the main material used temporarily in construction sites, can generate a large volume of waste, which is discarded as soon as the work is finished, with little reuse or recycling. Quantifying this waste can facilitate the management and control of the same, allowing the sustainability of a material which is important and noble for the development of civil construction. The aim of this study is to propose a model to estimate the generation of wood waste used temporarily in vertical construction, analyzing their design and execution characteristics. The research strategy used was the study of multiple cases, with data collection of works located in the metropolitan region of Porto Alegre, made through interviews, in situ observations and project analysis In all, the sample studied was formed by 22 works from 11 companies. As a result, statistical models were generated from multiple regression, containing the dependent variable - volume of wood waste - and the independent variables related to the project - number of floors, number of floors in subsoil, volume of concrete executed - and execution - linear length of siding in wood, the index use of wood in the production of collective protection equipment and temporary facilities. The proposed model has an adjusted R² of 0.893, indicating that it is able to explain 89.3% of the phenomenon. The variables that most impacted it were the number of floors and the number of floors in subsoil. The model allows construction companies to have greater control over the volume of wood waste generated by their works, and enables effective actions to be taken in favor of waste management, with greater control and reduction of waste. It can also be applied in other works from different regions, considering the local design and execution characteristics.
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Dias, Michele Ferreira. "Modelo para estimar a geração de resíduos na produção de obras residenciais verticais." Universidade do Vale do Rio dos Sinos, 2013. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4129.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
FAPERGS - Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
SINDUSCON/NH - Sindicato da Indústria da Construção Civil de Novo Hamburgo/RS
A significativa quantidade de resíduos gerada pelo setor da construção civil é hoje um grande problema ambiental a ser enfrentado pelo poder público e empresas construtoras. Para evitar ou reduzir a geração de resíduos é preciso atuar na origem, que pode estar desde o projeto até a fase de execução. O projeto consiste no passo inicial do empreendimento e tem influência significativa sobre o processo construtivo e produto final. Estudos apontam também que o sistema de gestão empregado por construtoras tem relação direta com a ocorrência de perdas, incluindo os resíduos. O conhecimento do índice de geração de resíduos é uma importante informação gerencial, com diferentes objetivos, desde a conscientização do problema, até para servir de base para o planejamento e implementação de ações voltadas à minimização da geração. Contudo, a quantificação dos resíduos de construção ainda pode ser considerada como um desafio, tendo em vista características peculiares da construção e a heterogeneidade dos resíduos. Observa-se muita variabilidade nos métodos empregados e variáveis consideradas, além das diferenças relativas à época e contextos pesquisados para geração dos índices e indicadores. Este trabalho propõe o desenvolvimento de um modelo estatístico para estimar a quantificação da geração de RCD em obras residenciais verticais, considerando características específicas das construtoras que atuam na região metropolitana de Porto Alegre-RS, investigando a influência do projeto e do sistema de produção. A estratégia de pesquisa consistiu num estudo de caso com múltiplas fontes de evidências, utilizando como ferramenta de análise estatística a regressão linear múltipla. A amostra é formada por dados de 20 obras residenciais verticais de 10 empresas que atuam na região metropolitana de Porto Alegre. Como resultado foi gerado um modelo estatístico, formado pela variável dependente (estimativa da quantidade de resíduo a ser gerada) e variáveis independentes relativas ao projeto (área do pavimento tipo, relação entre o número de pavimentos tipos e o número total de pavimentos e Índice econômico de compacidade) e ao sistema de produção (sistema construtivo e reaproveitamento de resíduos no canteiro). O modelo proposto obteve valor de R² ajustado = 0,69, permitindo explicar 69% da geração de resíduos em obras com características semelhantes, foi observada melhor aproximação da estimativa realizada pelo modelo quando comparado a algumas estimativas da literatura. A informação gerada através do modelo é importante na conscientização dos empresários locais e útil para basear ações no sentido de minimizar a geração, reciclar ou ações relativas ao destino final adequado, além de possibilitar comparações com informações sobre a geração de resíduos de outras localidades.
A significant amount of waste generated by the construction industry is now a major environmental problem to be faced by the government and construction companies. To avoid or reduce the generation of waste is necessary to work at the source, which may be from design to the implementation phase. The project consists in the initial step of the development and has significant influence on the construction process and final product. Studies also indicate that the management system employed by construction companies is directly related to the occurrence of losses, including waste. The knowledge of the waste generation rate is important management information, with different goals, since the awareness of the problem, even to serve as a basis for planning and implementation of actions aimed at minimizing the generation. However, the quantification of construction waste can still be considered as a challenge in view of the peculiar characteristics of the construction and the heterogeneity of the wastes. It is observed high variability in the methods employed and the variables considered, and the differences related to the time and contexts searched for generating indices and indicators. This work proposes the development of a statistical model to estimate the quantification of CDW generation in residential construction vertical, considering the specific characteristics of construction companies operating in the metropolitan area of Porto Alegre - RS, investigating the influence of design and production system. The research strategy consisted of a case study with multiple sources of evidence, using as a tool for statistical analysis with linear regression. The sample consists of data from 20 residential construction vertical of 10 companies operating in the metropolitan area of Porto Alegre. As a result generated a statistical model , formed by the dependent variable ( the estimated amount of waste to be generated ) and independent variables related to the project (area of the pavement type , relationship between the number of flooring types and the total number of floors and economic index compactness ) and production system ( building system and reuse of waste in construction ) . The proposed model obtained adjusted R² = 0.69 , allowing explain 69 % of waste generation in the works with similar characteristics was observed better approximation to the estimation made by the model when compared to some estimates of the literature . The information generated by the model is important in raising awareness of local entrepreneurs and useful to base actions to minimize the generation, recycling or actions relating to the appropriate destination, and enable comparisons with information on waste generation from other locations.
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Cagliari, Joice. "Função de pedotransferência para estimar o fósforo remanescente em solos, utilizando rede neural artificial." Universidade do Vale do Rio dos Sinos, 2010. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4670.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
O fósforo remanescente consiste na concentração de fósforo que permanece em solução após a agitação por 1 hora de uma amostra de solo com solução de CaCl2 0,01 mol L-1 contendo 60 mg L-1 P. O valor de fósforo remanescente pode ser utilizado como um bom indicador da capacidade de sorção aniônica de um solo haja vista ser mais sensível a sua composição mineralógica do que ao teor de sua fração argila. No Brasil, a utilização agronômica do fósforo remanescente é contemplada no sistema oficial de recomendação de fertilizantes e corretivos do Estado de Minas Gerais. O objetivo principal deste estudo foi o de desenvolver uma função de pedotransferência que permitisse estimar com razoável exatidão o valor de fósforo remanescente de solos representativos do Estado de São Paulo, a partir de outros atributos químicos de mais simples e/ou rotineira determinação laboratorial. Nesse contexto, duas funções de pedotransferência foram desenvolvidas com base em redes neurais artificiais (RNA) e análises de regressão linear múltipla (RLM), utilizando um banco de dados constituído por propriedades físicas e químicas de solos amostrados em diferentes localidades do Estado de São Paulo. As redes alimentadas adiante com múltiplas camadas foram utilizadas para desenvolver a função de pedotransferência baseada em RNA e a topologia foi determinada a partir de experimentos sucessivos. Os critérios de escolha da melhor rede neural foram, simultaneamente, o desempenho na etapa de treinamento, medido por meio do erro quadrático médio, e a capacidade de generalização, avaliada por meio de análises estatísticas entre os valores de Prem estimados e determinados analiticamente. A topologia da rede que melhor estimou o fósforo remanescente foi [3 14 1], ou seja, três neurônios na camada de entrada, quatorze em uma única camada intermediária e um na camada de saída; a função de ativação utilizada foi a sigmoidal logística, os valores de entrada foram normalizados entre [0;1] e o algoritmo de aprendizagem utilizado foi o resilient backpropagation. As três variáveis da camada de entrada foram o valor de pH medido em solução de NaF 1 mol L-1 (pH NaF), a soma de bases trocáveis (SB) e o teor de alumínio trocável (Al3+), sendo as duas últimas determinadas rotineiramente em análises de solo e a primeira de mais fácil e rápida obtenção que o fósforo remanescente. A função de pedotransferência baseada em RLM foi desenvolvida considerando as mesmas variáveis de entrada utilizadas na função de pedotransferência baseada em RNA. A comparação entre os desempenhos obtidos, para um mesmo conjunto de validação, mostrou que as funções de pedotransferência baseadas em redes neurais apresentam estimativas mais exatas do fósforo remanescente. Apesar do conjunto de dados utilizado não ser suficientemente abrangente para o estabelecimento de uma função de pedotransferência definitiva para a estimativa do fósforo remanescente, os resultados do presente trabalho indicam como promissor o desenvolvimento de um massivo banco de dados por meio do aproveitamento dos resultados analíticos continuamente gerados pelos vários laboratórios brasileiros dedicados à avaliação da fertilidade do solo e que contemple os valores de fósforo remanescente e pH NaF. Tal banco de dados permitirá o desenvolvimento de uma função de pedotransferência baseada em redes neurais artificiais cuja utilização possibilitará o cálculo imediato de valores suficientemente exatos de fósforo remanescente com razoável economia de recursos financeiros que seriam empregados na análise de um grande número de amostras.
The remaining phosphorus consists of the P concentration that remains in solution after shaking for 1 hour a soil sample with 0.01 mol L-1 CaCl2 containing 60 mg L-1 P. The remaining phosphorus values can be used as suitable indicators of the soil capacity of anion sorption due to be more dependable on the soil mineralogy than on the soil clay content. In Brazil, the remaining phosphorus is used as an ancillary variable in the official guidelines for determining fertilizer and amender requirements of agricultural soils of the Minas Gerais state. The main goal of this research was to develop a pedotransfer function (PTF) capable of providing fairly accurate estimates of remaining phosphorus values of representative soils of the São Paulo state from often-determined soil chemical properties and/or from other ones of easier determination. In this context, two pedotransfer functions were developed by using artificial neural networks (ANN) and multiple regression analysis (MRA) applied to a database formed by values of soil chemical and physical properties derived from soil surveys previously carried out in different locations of the São Paulo state. The multi-layer feedforward neural networks approach was used for the development of the ANN-based PTF being its topology determined from successive experiments. The simultaneous criteria adopted for choosing the best neural network were the performance during the training stage, measured by the mean squared error, and its capacity of providing accurate Prem values, which was evaluated by using a validation database in which statistical comparisons were done between the measured and estimated Prem values. The topology of the network that provided the most accurate estimates of the remaining phosphorus was [3 14 1], i.e., three neurons at the input layer, fourteen at a unique hidden layer and one neuron at the output layer; further development features included the use of the sigmoid logistic model as activation function, the input of data normalized in the [0;1] interval and the use of the resilient backpropagation learning algorithm. The three variables at the input layer were the soil pH value measured in 1 mol L-1 NaF (pH NaF), the sum of exchangeable bases (SB) and the soil content of exchangeable aluminum (Al3+), being the two last ones usually determined in soil test laboratories whereas the pH NaF determination is easier than the remaining phosphorus one. The MRA-based PTF was developed considering the same input variables of the ANN-based one, i.e., pH NaF, SB and Al3+. The comparisons performed with a same validation database showed that the pedotransfer function developed from artificial neural networks provided more accurate estimates of remaining phosphorus values. Despite the database used for the PTF development not be so comprehensive for the establishment of a definitive pedotransfer function for estimating remaining phosphorus values, the results of the present research indicate as promising the development of a massive database from chemical results often obtained by the Brazilian laboratories dedicated to soil fertility evaluation and that includes Prem and pH NaF values. This database will allow the development of a comprehensive ANN-based pedotransfer function capable of not only calculating suitable Prem values for practical applications but also reducing the expenses related to the analyses of a great number of soil samples.
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Portier, François. "Réduction de la dimension en régression." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00871049.
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Dans cette thèse, nous étudions le problème de réduction de la dimension dans le cadre du modèle de régression suivant Y=g(B X,e), où X est un vecteur de dimension p, Y appartient à R, la fonction g est inconnue et le bruit e est indépendant de X. Nous nous intéressons à l'estimation de la matrice B, de taille dxp où d est plus petit que p, (dont la connaissance permet d'obtenir de bonnes vitesses de convergence pour l'estimation de g). Ce problème est traité en utilisant deux approches distinctes. La première, appelée régression inverse nécessite la condition de linéarité sur X. La seconde, appelée semi-paramétrique ne requiert pas une telle condition mais seulement que X possède une densité lisse. Dans le cadre de la régression inverse, nous étudions deux familles de méthodes respectivement basées sur E[X f(Y)] et E[XX^T f(Y)]. Pour chacune de ces familles, nous obtenons les conditions sur f permettant une estimation exhaustive de B, aussi nous calculons la fonction f optimale par minimisation de la variance asymptotique. Dans le cadre de l'approche semi-paramétrique, nous proposons une méthode permettant l'estimation du gradient de la fonction de régression. Sous des hypothèses semi-paramétriques classiques, nous montrons la normalité asymptotique de notre estimateur et l'exhaustivité de l'estimation de B. Quel que soit l'approche considérée, une question fondamentale est soulevée : comment choisir la dimension de B ? Pour cela, nous proposons une méthode d'estimation du rang d'une matrice par test d'hypothèse bootstrap.
Books on the topic "Multiple Regression Estimate"
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Miksza, Peter, and Kenneth Elpus. Regression. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0010.
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This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including binary outcomes, is discussed. Particular attention is paid to the ability of regression to be used to estimate the effect of a predictor on an outcome while statistically “controlling” for the values of other observed variables.
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Logistic and Multiple Regression: A Two-Pronged Approach to Accurately Estimate Cost Growth in Major DoD Weapon Systems. Storming Media, 2004.
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Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Multifactorial analyses. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0010.
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Chapter 10 covers multifactorial analyses, including one-way analysis of variance and what is meant by reference categories, multiple regression, and logistic regression. The chapter describes how to present both unadjusted and adjusted estimates. The chapter includes analyses using Stata, SAS, SPSS, and R.
Book chapters on the topic "Multiple Regression Estimate"
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Agulló, José. "Exact algorithms for computing the least median of squares estimate in multiple linear regression." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 133–46. Hayward, CA: Institute of Mathematical Statistics, 1997. http://dx.doi.org/10.1214/lnms/1215454133.
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La Paglia, I., E. Di Gialleonardo, A. Facchinetti, M. Carnevale, and R. Corradi. "A Methodology to Estimate Railway Track Conditions from Vehicle Accelerations Based on Multiple Regression." In Lecture Notes in Civil Engineering, 203–10. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39109-5_21.
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Lai, T. L., Herbert Robbins, and C. Z. Wei. "Strong consistency of least squares estimates in multiple regression." In Herbert Robbins Selected Papers, 510–12. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5110-1_47.
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Moncayo, Steven, and Guillermo Ávila. "Landslide Travel Distances in Colombia from National Landslide Database Analysis." In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 315–25. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_24.
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AbstractThe analysis of landslide processes and consequent damages constitutes an important aspect in risk assessment. The potential reach zones of a landslide can be estimated by analyzing the behavior of past events under particular geological, geomorphological, and climatic conditions. Although landslide risk models have been developed for temperate zones, little information is available for tropical countries, so empirical equations are used without validation. In this study, a dataset comprising characteristic parameters for 123 landslides from the Andean region of Colombia was compiled from the digital inventory of the Colombian Geological Survey Mass Movement Information System (SIMMA). Empirical landslide travel-distance models were developed using simple and multiple regression techniques. The results revealed that the volume of the displaced mass, the slope angle, the maximum landslide height, and geomorphological environment were the predominant factors controlling the landslides travel distances in the study area. Similarly, a strong correlation was found between the planimetric area and landslide volume, validating the model of Iverson et al. (1998) (Iverson et al., in Geol Soc Am Bull 110:972–984, 1998). The proposed models show a reasonable fit between the observed and predicted values, and exhibited higher prediction capacity than other models in the literature. An example of application of the prediction equations developed here illustrates the procedure to delineate landslide hazard zones for different exceedance probabilities.
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Higano, Yoshiro. "Introduction: Real Estate Tax System and Real Estate Market in Japan." In New Frontiers in Regional Science: Asian Perspectives, 115–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8848-8_8.
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AbstractThis introduction summarizes chapters of Part II. In Chap. 10.1007/978-981-15-8848-8_9, Yamamoto (Jpn J Real Estate Sci 31:88–96, 2018) has compared between the street method, the asset valuation adopted for the imposition of property tax in Japan, and the computer-assisted mass appraisal (CAMA) method generally adopted in North America focusing on education and training of valuators. In Chap. 10.1007/978-981-15-8848-8_10, Yamazaki (Jpn J Real Estate Sci 31:97–101, 2018) argues that one of the major causes for relatively low density use of land in the city in Japan is the land tax system. He focuses on property tax and examines defects of the tax from view of economist. In Chap. 10.1007/978-981-15-8848-8_11, Kobayashi (Jpn J Real Estate Sci 31:129–138, 2018), taking an actual example, has examined difference between precise legal interpretation of ‘exemption from real estate acquisition tax due to purpose of use’ and taxation practices conducted by local administrative bodies. In Chap. 10.1007/978-981-15-8848-8_12, Shirakawa and Okoshi (Jpn J Real Estate Sci 31:88–96, 2017) have shown that the real estate companies were committed to transactions as dual agencies to whatsoever degree, and analyzed attributes of real estate brokerage companies which are able to be dual agencies and how such dual agency affects contract price.In Chap. 10.1007/978-981-15-8848-8_13, Ueno (Jpn J Real Estate Sci 31:97–105, 2017) has analyzed impacts of the macroeconomic conditions on the land price gradient curves which are estimated using real estate data of the Tokyo Metropolitan Area in 1970, 1976, 1985, 1988, 1994, 2008, 2010, and 2016. In Chap. 10.1007/978-981-15-8848-8_14, Komatsu (Jpn J Real Estate Sci 31:110–118, 2017) has analyzed impacts that refurbishment of existing apartment has on possible increase in rent using the multinomial probit model and the Tobit model. In Chap. 10.1007/978-981-15-8848-8_15, Hanazato (Jpn J Real Estate Sci 31:119–128, 2017) has shown that around 90% of condominium reconstruction cases are predictable using the estimated discriminant function in terms of objective real estate data only. In Chap. 10.1007/978-981-15-8848-8_16, Ota et al. (Jpn J Real Estate Sci 31:109–119, 2018) have analyzed determinants of rent for rental house, office, and shop within 10-min walking distance from Shibuya Station in Tokyo. Multiple regression analyses are conducted and have shown that space syntax (SS) measures (Hillier and Hanson, The Social Logic of Space. Cambridge University Press, Cambridge, 1984) significantly affect rent as well as conventional location attributes.
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"How to Estimate Regression Coefficients in MS Excel." In Multiple Linear Regression. India: Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630138.n2.
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Csathó, Botond Tamás, Csaba Endre Berky, and Bálint Péter Horváth. "Scattering Matrix-Based Dielectric Permittivity Estimation of Bulk Materials." In Electromagnetic Non-Destructive Evaluation (XXIV). IOS Press, 2023. http://dx.doi.org/10.3233/saem230003.
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In this paper we introduce the concept of a free-space dielectric permittivity characterization method. The technique is suited particularly for such cases where the dimensions of the test material is several times larger than the excitation wavelength. The proposed method estimates permittivity using a plane wave excitation and multiple receiver antennas. The received power of the antennas are combined in order to estimate the parameters effectively. The weight factors for the combination are determined using multiple-output multiple-regression. The input data of the regression model is calculated using numerical simulations for the different parameter combinations. Results show that the proposed method yields good results when the conductivity of the material is known in advance, and has a limited use when the complex permittivity has to be estimated.
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Bandyopadhyay, Arindam. "Matrix Algebra and their Application in Risk Prediction and Risk Monitoring." In Basic Statistics for Risk Management in Banks and Financial Institutions, 119–40. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.003.0005.
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Matrix Algebra concept and its numerous application in the measurement of credit risk as well as market risk have been elaborated in this chapter. A matrix is a rectangular array of elements. The transition matrix derived from the concept of matrix algebra has numerous applications in predicting bond valuation, value at risk analysis, and loan portfolio monitoring. The Markov chain process is used by reputed rating agencies and also the best practiced banks to predict probabilities of rating migration including analysis of default risk. It also enables a bank to estimate PD for different horizons and derive loan level expected loss for risk provisioning. The estimated PD is also helpful to detect significant increase in credit risk and estimate unexpected loss. The variance–covariance matrix is used in solving multiple regression equations and to find out the regression coefficient. This chapter also explains its role in estimating total portfolio risk computation.
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Cento, Veljanovski. "Part V Pass-On, 21 Proving Pass-on." In Cartel Damages. Oxford University Press, 2020. http://dx.doi.org/10.1093/law-ocl/9780198855163.003.0021.
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This chapter addresses the difficulty of establishing pass-on. Indeed, estimating pass-on is difficult and often impossible. Even where estimates of the pass-on rate can be generated, estimates of the overcharge are still required to quantify the amount of pass-on. For indirect purchasers, this will add to the difficulty because they may not have the necessary data and knowledge of successive upstream markets. There is also uncertainty to the standard of proof and evidential burden required to establish credible pass-on rates. However, there are a range of approaches that can be used to estimate or quantify the pass-on rate, which are set out in the European Commission’s Pass-on Guidelines. These include documentary evidence on firms’ pricing policies; economic theory/simulations; evidence on the way the direct and indirect purchasers have passed on cost increases in the past, arguing that they would react similarly to an overcharge; third party research on the way the industry has been passed on in the past; and statistical approaches either using multiple regression analysis, time series analysis, or event studies. The volume effect can be estimated using similar approaches although the Pass-on Guidelines suggest multiple regression analysis and the ‘elasticity approach’.
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Cento, Veljanovski. "Part IV Measurement, 15 Statistical Evidence." In Cartel Damages. Oxford University Press, 2020. http://dx.doi.org/10.1093/law-ocl/9780198855163.003.0015.
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This chapter discusses the econometric approach, and its forensic use and misuse. Econometrics is a set of statistical techniques which estimate the overall and individual effects of variables that affects a variable of interest whether it be prices, output, and so on. The major attraction of multiple regression analysis is its ability to simultaneously account for, estimate, and quantify the myriad factors which influence prices or output. Specifically, in the context of cartel damage, it holds out the possibility of estimating the ‘but for’ price adjusted for the non-cartel factors which affect prices in a systematic and credible way. There are three general multiple regression approaches to estimating overcharges: dummy variable (DVA), predictive, and difference-in-differences. Other statistical techniques can be useful in damages cases such as time series analysis and event studies.
Conference papers on the topic "Multiple Regression Estimate"
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Maier, Philipp M., and Sina Keller. "Machine Learning Regression on Hyperspectral Data to Estimate Multiple Water Parameters." In 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2018. http://dx.doi.org/10.1109/whispers.2018.8747010.
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Zhang, Ningyu, Guiyuan Liu, and Hongsheng Song. "Using hyperspectral image data to estimate soil mercury with stepwise multiple regression." In Eighth International Conference on Digital Image Processing (ICDIP 2016), edited by Charles M. Falco and Xudong Jiang. SPIE, 2016. http://dx.doi.org/10.1117/12.2244667.
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Okuno, Alex, and Alberto Ferreira. "Generalized linear tree: a flexible algorithm for predicting continuous variables." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021072420.
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Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated tree including variable selection, new hyperparameters optimization, and tree pruning. Our method, called Generalized linear tree (GLT), has shown to be competitive compared to other well-known regression methods in real datasets, with the advantages and estimation flexibility provided by GLMs.
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José Arnóbio de Araújo Júnior, Robson da Silva Magalhães, and Emerson Felipe Araújo Magalhães. "MULTIPLE LINEAR REGRESSION MODEL APPLIED TO A FRAMEWORK FOR INDICATORS ESTIMATE IN MAINTENANCE MANAGEMENT." In 23rd ABCM International Congress of Mechanical Engineering. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/cob-2015-0750.
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Zikos, Dimitrios, and Dhanashri Ostwal. "A Platform based on Multiple Regression to Estimate the Effect of in-Hospital Events on Total Charges." In 2016 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2016. http://dx.doi.org/10.1109/ichi.2016.72.
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Knop, Lauren, Guilherme Aramizo Ribeiro, and Mo Rastgaar. "Towards a Generalized Model of Multivariable Ankle Impedance During Standing Based on the Lower Extremity Muscle EMG." In 2019 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dmd2019-3315.
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The ankle mechanical impedance of healthy subjects was estimated during the standing pose while they co-contracted their lower-leg muscles. Subsequently, the impedance parameters were modeled as a function of the level of co-contraction using machine learning regression methods. From the experimental results, the average ankle stiffness coefficients in dorsi-plantar flexion (DP) showed more dependence to the muscle contraction than stiffness in inversion-eversion (IE): 4.6 Nm/rad per %MVC (percent of the maximum voluntary contraction) and 1.1 Nm/rad per %MVC, respectively. To accurately estimate the ankle impedance parameters as a function of the electromyography (EMG) signals, multiple EMG feature selection methods, regression models, and types of models were evaluated. Using a 1-vs-All model validation approach, the best regression model to fit the stiffness and damping in DP was the Least Square method with Regularization, and the best IE stiffness was the Gaussian Process Regression. No model was able to estimate the IE damping well, possibly because this parameter is not modulated with a changing co-contraction of the lower-leg muscles.
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Septianingrum, Sukma Ayu, M. Alfian Dzikri, M. Arief Soeleman, Pujiono Pujiono, and Muslih Muslih. "Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House." In 2022 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2022. http://dx.doi.org/10.1109/isemantic55962.2022.9920454.
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Alkinani, Husam Hasan, Abo Taleb Tuama Al-Hameedi, Shari Dunn-Norman, and Mustafa Adil Al-Alwani. "Statistical Models to Predict Tensile Strength from Unconfined Compressive Strength: Case Study from Southern Iraq." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205589-ms.
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Abstract Tensile strength (To) is an important parameter for creating geomechanical models, especially when tensile failure is the failure of interest. The most common way to estimate the tensile strength is by utilizing Brazilian tests. However, due to material limitation, cost, or time, To is sometimes assumed or estimated empirically. In this work, laboratory test data of To and Unconfined Compressive Strength (UCS) conducted for three zones in southern Iraq (Zubair sandstone, Zubair shale, and Nahr Umr shale) were utilized to create three regression models to estimate To from UCS. The reason for selecting UCS as the independent parameter is that static UCS, in most cases, has to be estimated from laboratory tests to create robust geomechanical models. In other words, UCS will be given the preference over Towhen there is the material limitation, cost, or time involved. The data of each zone were divided into training (80%) and testing (20%) to ensure the models can generalize for new data and avoid overfitting. Multiple least squares fits were tested, and linear least squares regression was selected since it provided the highest R2 and the lowest error. The models yielded training R2 of 0.983, 0.988, and 0.965 while the testing R2 were 0.978, 0.990, and 0.993 for Zubair sandstone, Zubair shale, and Nahr Umr shale, respectively. The errors were assessed using root mean squared error (RMSE) and mean absolute error (MAE), and they both have shown an acceptable margin of error for all three models. In short, the created three models showed the ability to estimate To from UCS when material limitation, cost, or time factors are involved or when executing a Brazilian test is not applicable. The proposed models can contribute to robust geomechanical models as well as minimizing cost, time, and material usage.
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Dilorenzo, Ednaldo, Felipe Furtado, Mauro Silva, Elisabeth Morais, and Gibeon Aquino. "Estimating LOC for OMA primitives: an approach based on correlation and regression analysis." In Simpósio Brasileiro de Engenharia de Software. Sociedade Brasileira de Computação, 2008. http://dx.doi.org/10.5753/sbes.2008.21341.
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Estimating API size is a very complex work since no approaches address particularly this development unit. Existing approaches are concentrated in estimating the software as a whole. Since many projects need to estimate API size, analyzing previous development is extremely necessary in order to identify which variables lead the API size and this can bring managers to derive cost and effort from it. This paper presents a case study from a OMA protocol client project where those variables were identified and the correlation and multiple regression techniques were used to find a equation that can predict the size of development of API modules for this specific project in Lines Of Code (LOC) unit size.
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Almeida, Paula, and Abel Carrasquilla. "Integrating geological attributes with a multiple linear regression of geophysical well logs to estimate the permeability of carbonate reservoirs in Campos basin - Southeastern Brazil." In International Conference and Exhibition, Barcelona, Spain, 3-6 April 2016. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2016. http://dx.doi.org/10.1190/ice2016-6267083.1.
Full textReports on the topic "Multiple Regression Estimate"
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Blampied, Nigel, Tariq Shehab, Elhami Nasr, and Laxmi Sindhu Samudrala. Preconstruction Support Cost Hours Estimating on Caltrans Pavement Rehabilitation Projects. Mineta Transportation Institute, May 2023. http://dx.doi.org/10.31979/mti.2023.2148.
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Because the construction phase accounts for the majority of project costs for pavement rehabilitation projects, most research on infrastructure project cost estimating focuses on that phase, rather than on the preconstruction phases. Nevertheless, costs incurred prior to construction, referred to in this report as "preconstruction costs" are significant and worthy of consideration (See Section 2.1 of the report for a more detailed and precise definition of preconstruction). In the 20202021 fiscal year, for instance, the California Department of Transportation (Caltrans) spent more than $169 million on preconstruction work for pavement rehabilitation projects. This report presents the results of a study of preconstruction cost estimating for pavement rehabilitation projects undertaken by Caltrans. It uses data on the 139 pavement rehabilitation projects for which Caltrans opened bids in the five-year period from April 26, 2016 to May 11, 2021. A data set was developed that combined the preconstruction hours for each project with the primary bid items for the pavement rehabilitation projects. Two models were developed to estimate preconstruction hours from the bid items, one using an Artificial Neural Network (ANN) and the other a parametric exponential model developed using multiple regression. The models had coefficients of determination of 0.85 and 0.80, respectively. Tools were then developed to assist professional users in validating their preconstruction cost estimates using each of the models. CTC staff or Caltrans can use these tools to evaluate the reasonableness of the preconstruction estimate on an individual project, or on the sum of an entire biennial SHOPP pavement rehabilitation portfolio, in order to assure the most efficient use of infrastructure funding to best serve the community's transportation needs.
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Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.
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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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Ghanghas, Ankit, Sayan Dey, and Venkatesh Merwade. Development of a Multiple Water Course Joint Probability Analysis Procedure for Indiana Watersheds. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317616.
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The design of hydraulic structures located near a confluence of two streams must take into consideration the flows from both of the streams. A hydraulic structure located on a small tributary that drains into a large river immediately downstream is not just affected by the flow in the tributary, but also by the backwater flow from the downstream river. Currently INDOT uses a tabular summary (Table 1.1 and Table 7.3 in the HEC-22 manual) of joint probabilities of coincident flows in designing hydraulic structures at confluences. However, the source of the table is unknown, and the tabular summary provides coincidental flows for only 1% and 10% probabilities, and thus it cannot be used directly for other probabilities. This study analyzed the interdependence of flows in mainstream and tributary and then developed a Gumbel-Hougard Copula-based procedure for estimating joint probabilities for confluences in Indiana. The study found that the mainstream and tributary streamflow are significantly correlated with Kendall’s Tau varying generally ranging from 0.5 to 0.8. Furthermore, the Kendall’s Tau, which is the key parameter for Gumbel-Hougard Copula, was found to be significantly related to drainage area ratio (DAR). Regression-based equations between DAR and τ are used as a basis to relate DAR to joint probabilities at confluences. The study also found that the currently used tabular summary (Table 1.1 and Table 7.3 in HEC-22 manual) resulted in significantly conservative design estimates and therefore overdesigned structures.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.
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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) determine causative polymorphisms based on concordance between the bulls’ genotypes for specific polymorphisms and their status for a QTL; (4) validate putative quantitative trait variants by genotyping a sample of Israeli Holstein cows; and (5) perform gene expression analysis using statistical methodologies, including determination of signatures of selection, based on somatic cells of cows that are homozygous for contrasting quantitative trait variants; and (6) analyze genes with putative quantitative trait variants using data mining techniques. Current methods for genomic evaluation are based on population-wide linkage disequilibrium between markers and actual alleles that affect traits of interest. Those methods have approximately doubled the rate of genetic gain for most traits in the U.S. Holstein population. With determination of causative polymorphisms, increasing the accuracy of genomic evaluations should be possible by including those genotypes as fixed effects in the analysis models. Determination of causative polymorphisms should also yield useful information on gene function and genetic architecture of complex traits. Concordance between QTL genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects that are segregating in the U.S. Holstein population; a probability of <10²⁰ was used to accept the null hypothesis that no segregating gene within the chromosomal segment was affecting the trait. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome. Variant sites were identified from previous studies (such as the 1000 Bull Genomes Project) and from DNA sequencing of bulls unique to this project, which is one of the largest marker variant surveys conducted for the Holstein breed of cattle. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: (1) complete or nearly complete concordance, (2) nominal significance of the polymorphism effect after correction for all other polymorphisms, and (3) marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. The missense polymorphism Phe279Tyr in GHR at 31,909,478 base pairs on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage, 12 additional missensepolymorphisms on chromosome 14 were found that had nearly complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The markers used in routine U.S. genomic evaluations were increased from 60,000 to 80,000 by adding markers for known QTLs and markers detected in BARD and other research projects. Objectives 1 and 2 were completely accomplished, and objective 3 was partially accomplished. Because no new clear-cut causative polymorphisms were discovered, objectives 4 through 6 were not completed.
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Brosh, Arieh, David Robertshaw, Yoav Aharoni, Zvi Holzer, Mario Gutman, and Amichai Arieli. Estimation of Energy Expenditure of Free Living and Growing Domesticated Ruminants by Heart Rate Measurement. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580685.bard.
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Research objectives were: 1) To study the effect of diet energy density, level of exercise, thermal conditions and reproductive state on cardiovascular function as it relates to oxygen (O2) mobilization. 2) To validate the use of heart rate (HR) to predict energy expenditure (EE) of ruminants, by measuring and calculating the energy balance components at different productive and reproductive states. 3) To validate the use of HR to identify changes in the metabolizable energy (ME) and ME intake (MEI) of grazing ruminants. Background: The development of an effective method for the measurement of EE is essential for understanding the management of both grazing and confined feedlot animals. The use of HR as a method of estimating EE in free-ranging large ruminants has been limited by the availability of suitable field monitoring equipment and by the absence of empirical understanding of the relationship between cardiac function and metabolic rate. Recent developments in microelectronics provide a good opportunity to use small HR devices to monitor free-range animals. The estimation of O2 uptake (VO2) of animals from their HR has to be based upon a consistent relationship between HR and VO2. The question as to whether, or to what extent, feeding level, environmental conditions and reproductive state affect such a relationship is still unanswered. Studies on the basic physiology of O2 mobilization (in USA) and field and feedlot-based investigations (in Israel) covered a , variety of conditions in order to investigate the possibilities of using HR to estimate EE. In USA the physiological studies conducted using animals with implanted flow probes, show that: I) although stroke volume decreases during intense exercise, VO2 per one heart beat per kgBW0.75 (O2 Pulse, O2P) actually increases and measurement of EE by HR and constant O2P may underestimate VO2unless the slope of the regression relating to heart rate and VO2 is also determined, 2) alterations in VO2 associated with the level of feeding and the effects of feeding itself have no effect on O2P, 3) both pregnancy and lactation may increase blood volume, especially lactation; but they have no effect on O2P, 4) ambient temperature in the range of 15 to 25°C in the resting animal has no effect on O2P, and 5) severe heat stress, induced by exercise, elevates body temperature to a sufficient extent that 14% of cardiac output may be required to dissipate the heat generated by exercise rather than for O2 transport. However, this is an unusual situation and its affect on EE estimation in a freely grazing animal, especially when heart rate is monitored over several days, is minor. In Israel three experiments were carried out in the hot summer to define changes in O2P attributable to changes in the time of day or In the heat load. The animals used were lambs and young calves in the growing phase and highly yielding dairy cows. In the growing animals the time of day, or the heat load, affected HR and VO2, but had no effect on O2P. On the other hand, the O2P measured in lactating cows was affected by the heat load; this is similar to the finding in the USA study of sheep. Energy balance trials were conducted to compare MEI recovery by the retained energy (RE) and by EE as measured by HR and O2P. The trial hypothesis was that if HR reliably estimated EE, the MEI proportion to (EE+RE) would not be significantly different from 1.0. Beef cows along a year of their reproductive cycle and growing lambs were used. The MEI recoveries of both trials were not significantly different from 1.0, 1.062+0.026 and 0.957+0.024 respectively. The cows' reproductive state did not affect the O2P, which is similar to the finding in the USA study. Pasture ME content and animal variables such as HR, VO2, O2P and EE of cows on grazing and in confinement were measured throughout three years under twenty-nine combinations of herbage quality and cows' reproductive state. In twelve grazing states, individual faecal output (FO) was measured and MEI was calculated. Regression analyses of the EE and RE dependent on MEI were highly significant (P<0.001). The predicted values of EE at zero intake (78 kcal/kgBW0.75), were similar to those estimated by NRC (1984). The EE at maintenance condition of the grazing cows (EE=MEI, 125 kcal/kgBW0.75) which are in the range of 96.1 to 125.5 as presented by NRC (1996 pp 6-7) for beef cows. Average daily HR and EE were significantly increased by lactation, P<0.001 and P<0.02 respectively. Grazing ME significantly increased HR and EE, P<0.001 and P<0.00l respectively. In contradiction to the finding in confined ewes and cows, the O2P of the grazing cows was significantly affected by the combined treatments (P<0.00l ); this effect was significantly related to the diet ME (P<0.00l ) and consequently to the MEI (P<0.03). Grazing significantly increased O2P compared to confinement. So, when EE of grazing animals during a certain season of the year is estimated using the HR method, the O2P must be re measured whenever grazing ME changes. A high correlation (R2>0.96) of group average EE and of HR dependency on MEI was also found in confined cows, which were fed six different diets and in growing lambs on three diets. In conclusion, the studies conducted in USA and in Israel investigated in depth the physiological mechanisms of cardiovascular and O2 mobilization, and went on to investigate a wide variety of ruminant species, ages, reproductive states, diets ME, time of intake and time of day, and compared these variables under grazing and confinement conditions. From these combined studies we can conclude that EE can be determined from HR measurements during several days, multiplied by O2P measured over a short period of time (10-15 min). The study showed that RE could be determined during the growing phase without slaughtering. In the near future the development microelectronic devices will enable wide use of the HR method to determine EE and energy balance. It will open new scopes of physiological and agricultural research with minimizes strain on animals. The method also has a high potential as a tool for herd management.
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Multiple-regression equations to estimate peak-flow frequency for streams in Hays County, Texas. US Geological Survey, 1995. http://dx.doi.org/10.3133/wri954019.
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