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Tong, Fan. "Capacity demand and climate in Ekerö : Development of tool to predict capacity demand underuncertainty of climate effects." Thesis, KTH, Elektroteknisk teori och konstruktion, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-152522.
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The load forecasting has become an important role in the operation of power system, and several models by using different techniques have been applied to solve these problems. In the literature, the linear regression models are considered as a traditional approach to predict power consumption, and more recently, the artificial neural network (ANN) models have received more attention for a great number of successful and practical applications. This report introduces both linear regression and ANN models to predict the power consumption for Fortum in Ekerö. The characteristics of power consumption of different kinds of consumers are analyzed, together with the effects of weather parameters to power consumption. Further, based on the gained information, the numerical models of load forecasting are built and tested by the historical data. The predictions of power consumption are focus on three cases separately: total power consumption in one year, daily peak power consumption during winter and hourly power consumption. The processes of development of the models will be described, such as the choice of the variables, the transformations of the variables, the structure of the models and the training cases of ANN model. In addition, two linear regression models will be built according to the number of input variables. They are simple linear regression with one input variable and multiple linear regression with several input variables. Comparison between the linear regression and ANN models will be carried out. In the end, it finds out that the linear regression obtains better results for all the cases in Ekerö. Especially, the simple linear regression outperforms in prediction of total power consumption in one year, and the multiple linear regression is better in prediction of daily peak load during the winter.
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.
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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)
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Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.
In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.
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Silva, Mariellen Vital da [UNESP]. "Um modelo matemático para estudo de otimização do consumo de energia elétrica." Universidade Estadual Paulista (UNESP), 2007. http://hdl.handle.net/11449/87261.
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Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção.
In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production.
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Huschens, Stefan. "Einführung in die Ökonometrie." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-222629.
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Die Kapitel 1 bis 6 im ersten Teil dieses Skriptes beruhen auf einer Vorlesung Ökonometrie I, die zuletzt im WS 2001/02 gehalten wurde, die Kapitel 7 bis 16 beruhen auf einer Vorlesung Ökonometrie II, die zuletzt im SS 2006 gehalten wurde. Das achte Kapitel enthält eine komprimierte Zusammenfassung der Ergebnisse aus dem Teil Ökonometrie I.
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Silva, Mariellen Vital da. "Um modelo matemático para estudo de otimização do consumo de energia elétrica /." Ilha Solteira : [s.n.], 2007. http://hdl.handle.net/11449/87261.
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Resumo: Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção.Abstract: In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production.
Orientador: Francisco Villarreal Alvarado
Coorientador: Antonio Padilha Feltrin
Banca: Evaristo Bianchini Sobrinho
Banca: José Carlos de Melo Vieira Júnior
Mestre
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Karsburg, Roberta Machado, and Roberta Machado Karsburg. "Precipitação e velocidade do vento na oscilação dos níveis d’água do canal São Gonçalo-RS." Universidade Federal de Pelotas, 2016. http://repositorio.ufpel.edu.br:8080/handle/prefix/3693.
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Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul - FAPERGS
O canal São Gonçalo se configura como um importante curso d’água, pertencente a bacia hidrográfica Mirim-São Gonçalo, que tem área de 25.000 km² e situa-se na região costeira no estado do Rio Grande do Sul. Aliado a isso, ele une a laguna dos Patos, a qual mantém conexão direta com o oceano Atlântico, à lagoa Mirim, considerada como um grande reservatório de água doce no sul do Brasil. Está situado em uma região de planície, de baixas declividades e apresenta grande complexidade e sensibilidade às oscilações dos níveis d’água, tanto em função do regime e direção de ventos como do regime de chuvas. Com isso, este trabalho teve como objetivo avaliar a influência do vento e da precipitação na oscilação do nível d`água à jusante da barragem eclusa do canal São Gonçalo-RS. A hipótese foi que a velocidade e direção do vento, juntamente com a precipitação, influenciam à oscilação dos níveis do canal São Gonçalo. Para alcançar os objetivos, foram empregados métodos de regressão linear múltipla e simples entre o nível d’água, precipitação e velocidades do vento à 2 e 7 m de altura e velocidade máxima. Para determinar a significância dos modelos de regressões lineares, foi utilizado o teste t de Student, para a verificação quais variáveis que são influenciadoras da oscilação do nível. Por fim, para indicar o grau de precisão dos modelos de regressões lineares avaliados, aplicou-se a metodologia do erro relativo médio quadrático. As direções de ventos que mostraram-se mais influenciadoras no processo de oscilação dos níveis d’água à jusante do canal São Gonçalo, foram as de sudeste (SE), sul (S) e oeste (O). A variável com maior influência no processo de oscilação dos níveis d’água à jusante da barragem eclusa do canal São Gonçalo, foi a velocidade máxima do vento (VMáx).
São Gonçalo is an important watercourse, belonging to the Mirim-São Gonçalo hydrographic basin, which has an area of 25,000 km² and is located in the coastal region of the state of Rio Grande do Sul. The Patos lagoon, which maintains a direct connection with the Atlantic Ocean, to Mirim lagoon, considered as a large reservoir of fresh water in southern Brazil. It’s situated in a lowland lowland region and presents great complexity and sensitivity to the fluctuations of water levels, both due to the regime and direction of the winds and the rainfall regime. The objective of this work was to evaluate the influence of wind and precipitation on the oscillation of the water level downstream of the São Gonçalo channel dam. The hypothesis was that the velocity and direction of the wind, along with the precipitation, influence the oscillation of the levels of the São Gonçalo channel. To achieve the objectives, multiple linear simple regression methods were used between water level, precipitation and wind velocities at 2 and 7 m in height and at maximum velocity. To determine the significance of the linear regression models, the Student's t test was used to verify which variables are influencing the level oscillation. Finally, to indicate the degree of precision of the linear regression models evaluated, the methodology of the relative mean square error was applied. The directions of the winds that were most influential in the process of oscillation of the water levels downstream of the São Gonçalo channel were those of the southeast (SE), south (S) and west (O). The variable with the greatest influence on the oscillation process of the water levels downstream of the São Gonçalo channel dam was the maximum wind speed.
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Brodbeck, William Joseph. "The Effect of Readability on Simple Linear Regression." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1591867761661656.
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Guan, Tianyuan. "Sample Size Calculations in Simple Linear Regression: A New Approach." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667392849137.
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Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.
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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were created and their functionality was tested, but only seven models were better than chance i.e. more than 50 % in the right direction. To determine the most suitable model out of the remaining seven, Akaike’s Information Criterion (AIC), was applied. The covariates used in the final model were; Dow Jones closing price, Shanghai opening price, conjuncture, oil price, share’s opening price, share’s highest price, share’s lowest price, lending rate, reports, positive/negative insider trading, payday, positive/negative price target, number of completed transactions during one day, OMX Stockholm closing price, TCW index, increasing closing price three days in a row and decreasing closing price three days in a row. The maximum average deviation between the predicted closing price and the real closing price of all the 44 shares predicted were 6,60 %. In predicting the correct direction (increase or decrease) of the 44 shares an average of 61,72 % were achieved during the time period 2012-02-22 to 2013-02-20. If investing 50.000 SEK in each company i.e. a total investment of 2.2 million SEK, the total yield when using the regression model during the year 2012-02-22 to 2013-02-20 would have been 259.639 SEK (11,80 %) compared to 184.171 SEK (8,37 %) if the shares were never to be traded with during the same period of time. Of the 44 companies analysed, 31 (70,45 %) of them were profitable when using the regression model during the year compared to 30 (68,18 %) if the shares were never to be sold during the same period of time. The difference in yield in percentage between the model and keeping the shares for the year was 40,98 %.
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Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.
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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regression model acquired through a model reduction procedure showed that mostly structural covariates such as the academic background of pupils, percentage of female pupils and the percentage with Swedish background had a statistically significant impact on the academic performances of the students. R2 adjusted of the final model was 0.5289. The multiple regression model was discussed by referencing to previous research. In addition, the strategic management performance framework known as Balanced Scorecard which was introduced by Robert S. Kaplan and David P. Norton was used to discuss relevant key performance indicators to achieve the strategic objectives of schools.Detta examensarbete, inom ämnet för matematisk statistik och industriell ekonomi, genomfördes med syftet att analysera avgångsbetygen för år 9 i den svenska skolan. Syftet var att förstå vilka variabler som hade en statistisk signifikant påverkan på elevers avgångsbetyg, så kommuner kan förstå vilka variabler som är viktiga för att förbättra de genomsnittliga skolresultaten. En regressionsanalys utfördes, på data från Skolverket, för att se vilka variabler som var statistiskt signifikanta. Den slutgiltiga regressionsmodellen, erhållen genom iterativ reducering av variabler, visade att främst strukturella kovariat, som akademisk bakgrund hos elever, andel kvinnliga studenter och andel studenter med svensk bakgrund hade en signifikant betydelse på studenters akademiska resultat. Justerad R2 var 0.5289 för den slutgiltiga modellen. I diskussionen utvärderades modellen utifrån tidigare forskning. Vidare användes teorin om balanserat styrkort, utvecklat av Robert S. Kaplan och David P. Norton, för att diskutera relevanta nyckeltal för att uppnå strategiska mål för skolan.
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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.
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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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Gustafsson, Alexander, and Sebastian Wogenius. "Modelling Apartment Prices with the Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146735.
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This thesis examines factors that are of most statistical significance for the sales prices of apartments in the Stockholm City Centre. Factors examined are address, area, balcony, construction year, elevator, fireplace, floor number, maisonette, monthly fee, penthouse and number of rooms. On the basis of this examination, a model for predicting prices of apartments is constructed. In order to evaluate how the factors influence the price, this thesis analyses sales statistics and the mathematical method used is the multiple linear regression model. In a minor case-study and literature review, included in this thesis, the relationship between proximity to public transport and the prices of apartments in Stockholm are examined. The result of this thesis states that it is possible to construct a model, from the factors analysed, which can predict the prices of apartments in Stockholm City Centre with an explanation degree of 91% and a two million SEK confidence interval of 95%. Furthermore, a conclusion can be drawn that the model predicts lower priced apartments more accurately. In the case-study and literature review, the result indicates support for the hypothesis that proximity to public transport is positive for the price of an apartment. However, such a variable should be regarded with caution due to the purpose of the modelling, which differs between an individual application and a social economic applicationDenna uppsats undersöker faktorer som är av störst statistisk signifikans för priset vid försäljning av lägenheter i Stockholms innerstad. Faktorer som undersöks är adress, yta, balkong, byggår, hiss, kakelugn, våningsnummer, etage, månadsavgift, vindsvåning och antal rum. Utifrån denna undersökning konstrueras en modell för att predicera priset på lägenheter. För att avgöra vilka faktorer som påverkar priset på lägenheter analyseras försäljningsstatistik. Den matematiska metoden som används är multipel linjär regressionsanalys. I en mindre litteratur- och fallstudie, inkluderad i denna uppsats, undersöks sambandet mellan närhet till kollektivtrafik och priset på läagenheter i Stockholm. Resultatet av denna uppsats visar att det är möjligt att konstruera en modell, utifrån de faktorer som undersöks, som kan predicera priset på läagenheter i Stockholms innerstad med en förklaringsgrad på 91 % och ett två miljoner SEK konfidensintervall på 95 %. Vidare dras en slutsats att modellen preciderar lägenheter med ett lägre pris noggrannare. I litteratur- och fallstudien indikerar resultatet stöd för hypotesen att närhet till kollektivtrafik är positivt för priset på en lägenhet. Detta skall dock betraktas med försiktighet med anledning av syftet med modelleringen vilket skiljer sig mellan en individuell tillämpning och en samhällsekonomisk tillämpning.
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Ulgen, Burcin Emre. "Estimation In The Simple Linear Regression Model With One-fold Nested Error." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606171/index.pdf.
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In this thesis, estimation in simple linear regression model with one-fold nested error is studied.
To estimate the fixed effect parameters, generalized least squares and maximum likelihood estimation procedures are reviewed. Moreover, Minimum Norm Quadratic Estimator (MINQE), Almost Unbiased Estimator (AUE) and Restricted Maximum Likelihood Estimator (REML) of variance of primary units are derived.
Also, confidence intervals for the fixed effect parameters and the variance components are studied. Finally, the aforesaid estimation techniques and confidence intervals are applied to a real-life data and the results are presented
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Kinns, David Jonathan. "Multiple case influence analysis with particular reference to the linear model." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368427.
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Galijasevic, Amar, and Josef Tegbaru. "Can IPO first day returns be predicted? A multiple linear regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254293.
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During the last three years the Swedish stock market has showed a strong upwards movement from the lows of 2016. At the same time the IPO activity has been large and a lot of the offerings have had a positive return during the first day of trading in the market. The goal of this study is to analyze if there is any particular IPO specific data that has a correlation with the first day return and if it can be used to predict the first day return for future IPO’s. If any regressors were shown to have correlation with the first day return, the goal is also to find a subset of regressors with even higher predictability. Then to classify which regressors show the highest correlation with a large positive return. The method which has been used is a multiple linear regression with IPO-data from the period 2017-2018. The results from the study imply that none of the chosen regressors show any significant correlation with the first day return. It is a complicated process which might be difficult to simplify and quantify into a regression model, but further studies are needed to draw a conclusion if there are any other qualitative factors which correlate with the first day return.Under de senaste tre åren har den svenska aktiemarknaden visat en kraftigt uppåtgående rörelse från de låga nivåerna 2016. Samtidigt har det varit hög IPO-aktivitet, där många noteringar har haft en positiv avkastning under den första handelsdagen. Målet med denna studie är att analysera om det finns särskilda IPO-specifika faktorer som påvisar samband med avkastningen från första handelsdagen och om det kan användas för att förutsäga utvecklingen under första handelsdagen för framtida noteringar. Om regressorerna visade korrelation är målet sedan att ta fram de bästa av dessa för att se om det ökar modellens säkerhet. Vidare var det av intresse att visa vilka regressorer som korrelerar med en positiv avkastning. Metoden som användes var en multipel linjär regression med historisk data från perioden 2017-2018. Studiens resultat visar att ingen av de valda regressorerna har någon signifikant korrelation med avkastningen under första handelsdagen. Börsintroduktioner är komplicerade processer som kan vara svåra att förenkla och kvantifiera i en regressionsmodell, men ytterligare studier behövs för att dra en slutsats om det finns andra kvalitativa faktorer som kan förklara utvecklingen under första handelsdagen.
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Basha, Elizabeth (Elizabeth Ann). "In-situ prediction on sensor networks using distributed multiple linear regression models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60096.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 199-208).
Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation where complex models are replaced by in-situ sensing and communication. These prediction models utilize low-computation, low-communication, and distributed algorithms suited to autonomous operation and multiple applications. We achieve this through development of new algorithms that enable distributed computation of the pseudo inverse of a matrix on a sensor network, thereby enabling a wide range of prediction methods. We apply these models to three different application areas: (1) river flooding for early warning, (2) solar recharging current for power management, and (3) job congestion prediction on multi-function device networks for achieving quality of service. Additionally, we use these applications to explore other aspects of sensor networks: river flooding to design a predictive environmental monitoring sensor network, solar current to develop a dynamic version of the model for better fault tolerance, and job congestion to explore modeling multi-function device networks. For each, we comprehensively tested the full solutions. We implemented the river flood prediction and solar current prediction solutions on two different sensor network platforms with full field deployments; we had a final test of over 5 weeks operation for both. Overall, we achieve the following contributions: (1) distributed algorithms for computing a matrix pseudoinverse and multiple linear regression model on a sensor network, (2) three applications of these algorithms with associated field experiments demonstrating their versatility, (3) a sensor network architecture and implementation for river flood prediction as well as other applications requiring real-time data and a low node count to geographic area ratio, and (4) a MFD simulator predicting and resolving congestion.
by Elizabeth Ann Basha.
Ph.D.
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Murff, Elizabeth J. Tipton. "On the efficiency of ranked set sampling relative to simple random sampling for estimating the ordinary least squares parameters of the simple linear regression model /." Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008403.
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Tao, Jinxin. "Comparison Between Confidence Intervals of Multiple Linear Regression Model with or without Constraints." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/404.
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Regression analysis is one of the most applied statistical techniques. The sta- tistical inference of a linear regression model with a monotone constraint had been discussed in early analysis. A natural question arises when it comes to the difference between the cases of with and without the constraint. Although the comparison be- tween confidence intervals of linear regression models with and without restriction for one predictor variable had been considered, this discussion for multiple regres- sion is required. In this thesis, I discuss the comparison of the confidence intervals between a multiple linear regression model with and without constraints.
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Lin, Xiaojun. "Multiple Random Slope and Fixed Intercept Linear Regression Models for Pavement Condition Forecasting." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1430364217.
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Denés, August, and Martin Lindblom. "Determining Important Factors for Profiability in Dietary Supplement Retailing by Multiple Linear Regression." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188995.
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This thesis in applied statistics and industrial economics examined which factors and strategies that had a statistically significant impact on profitability, within the business to consumer dietary supplement market. The data this thesis was based on consisted of several annual reports from the year 2011 to 2015ånd other strategic information. The data included 19 different dietary supplement retailers on the Swedish market. In order to establish which factors had a significant impact on profitability, a linear regression was used. The result was an identified linear relationship between Operating Margin and the covariates Solidity, Average Salary, Only Own Brand, Free Returns, Student Discounts and Chat. A market analysis was then performed using Porter's five forces and a PEST analysis. The analysis concluded that the market is attractive but there are a few uncertainties surrounding the future development of the dietary supplement retailing market.Det här kandidatexamensarbetet i tillämpad statistik och industriell ekonomi undersökte vilka faktorer och strategier som hade en statistiskt signifikant påverkan på lönsamhet, inom kosttillskottsåterförsäljning på den svenska konsumentmarknaden. Data som rapporten är baserad på kom ifrån flertalet årsredovisningar mellan åren 2011 och 2015 samt annan strategisk information. Studien inkluderade 19 olika företag på den svenska kosttillskottsåterförsäljningsmarknaden. För att identifiera de påverkande faktorerna tillämpades linjär regression. Resultat visade att det finns ett linjärt samband mellan Rörelsemarginal och kovariaten Soliditet, Snittlön, Endast Eget Märke, Fria Returer, Studentrabatt och Chatt. Sedan utfördes en marknadsanalys med hjälp av Porters's femkraftsmodell samt PEST-analys. Anslysen fastslog att marknaden är attraktiv, men det finns några osäkerheter rörande den framtida utvecklingen på kosttillskottsåterförsäljningsmarknaden.
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Rudin, Pierre. "Football result prediction using simple classification algorithms, a comparison between k-Nearest Neighbor and Linear Regression." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187659.
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Ever since humans started competing with each other, people have tried to accurately predict the outcome of such events. Football is no exception to this and is extra interesting as subject for a project like this with the ever growing amount of data gathered from matches these days. Previously predictors had to make there predictions using there own knowledge and small amounts of data. This report will use this growing amount of data and find out if it is possible to accurately predict the outcome of a football match using the k-Nearest Neighbor algorithm and Linear regression. The algorithms are compared on how accurately they predict the winner of a match, how precise they predict how many goals each team will score and the accuracy of the predicted goal difference. The results are graphed and presented in tables. A discussion analyzes the results and draw the conclusion that booth algorithms could be useful if used with a good model, and that Linear Regression out performs k-NN.Ända sedan vi människor började tävla mot varandra, har folk försökt förutspå vinnaren i tävlingarna. Fotboll är inget undantag till detta och är extra intressant för den här studien då den tillgängliga mängden data från fotbollsmatcher ständigt ökar. Tidigare har egna kunskaper och små mängder data använts för att förutspå resultaten. Den här rapporten kommer dra nytta av den växande mängden data för att ta reda på om det är möjligt att med hjälp av k-Nearest Neighbor algoritmen och Linjär regression förutspå resultat i fotbollsmatcher. Algoritmerna kommer jämföras utifrån hur exakt de förutspår vinnaren i matcher, hur många mål de båda lagen gör samt hur precist algoritmerna förutspår målskilnaden i matcherna. Resultaten presenteras både i grafer och i tabeller. En diskusion förs för att analysera resultaten och kommer fram till att båda algoritmerna kan vara användbara om modelen är välkonstruerad, och att Linjär regression är bättre lämpad än k-NN.
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Alt, Raimund. "Multiple hypotheses testing in the linear regression model with applications to economics and finance /." Göttingen : Cuvillier, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=013081924&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Kareflod, Michaela, and Jennifer Ljungquist. "A Study of Hot el Occupancy : Using Multiple Linear Regression and Market Strategy Analysis." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189017.
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This paper is based on collaboration between a company called StayAt HotelApart AB and two KTH students. It examines which factors that are influencing the hotel’s occupancy and how this may be increased by enhancing the market strategy. The aim is to provide a foundation for strategy development to the company. The study is performed by connecting applied mathematics with industrial management. The mathematical part is based on a multiple linear regression on occupancy with historical data from 2011 to 2016 mainly collected from StayAt. The analysis of the market strategy is performed by means of the mathematical results and by using two marketing models, SWOT analysis and 4P’s. The result shows that relative price, weather, high- and low season for the hotel, months on market, occupancy for the competitive set, location and market shares are significant factors influencing the hotel’s occupancy. The main recommendations concluded from the analysis of the market strategy are to put effort on digitalisation, visualising the brand, publications, CSR initiatives, exploiting existing resources and carefully considering timing of marketing.Den här uppsatsen baseras på ett samarbete mellan företaget StayAt HotelApart AB och två KTH- studenter. Den utvärderar vilka faktorer som påverkar hotellets beläggning och hur denna kan öka genom en förbättrad marknadsstrategi. Syftet är att leverera en grund för strategiutveckling till företaget. Studien är genomförd genom att sammankoppla tillämpad matematik med industriell ekonomi. Den matematiska delen baseras på en regressionsanalys av hotellets beläggning med historisk data från 2011 till 2016 som främst är försedd av StayAt. Analysen av marknadsstrategin är genomförd med hjälp av de matematiska resultaten samt genom att applicera två modeller inom marknadsföring, SWOT analys och 4P. Resultaten visar att relativt pris, väder, hög- och lågsäsong för hotellet, månader på marknaden, beläggning för konkurrenter, läge och marknadsandelar är signifikanta faktorer som påverkar hotellets beläggning. De primära rekommendationerna som tagits fram utifrån analysen av marknadsstrategin är att lägga resurser på digitalisering, publikationer och CSR initiativ, att visualisera varumärket, utnyttja existerande resurser samt att grundligt överlägga timing av marknadsföring
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Yeasmin, Mahbuba 1965. "Multiple maxima of likelihood functions and their implications for inference in the general linear regression model." Monash University, Dept. of Econometrics and Business Statistics, 2003. http://arrow.monash.edu.au/hdl/1959.1/5821.
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Palmgren, Elin, and Natasha Nanakorn. "The Impact of Macroeconomic Variables on Stock Return in Different Industries - A Multiple Linear Regression." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254204.
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Macroeconomics constitute a central part of fundamental analysis of stock markets and consequently the relationship between macroeconomic variables and stock markets is far from questioned. However, there is no general consensus regarding neither the extent of this relationship nor whether the relationship varies amongst industries. The aim of this thesis is therefore to determine the macroeconomic variables most important in explaining variations in stock return within two separate industries and furthermore the share of these variations solely accounted for by macroeconomic variables. To this end, a multiple linear regression approach is used and Nasdaq indexes OMX Stockholm Industrial Goods & Services and OMX Stockholm Banks are used as proxies for the two selected industries. The final result of this analysis is that the variables repo rate, SEK/EUR exchange rate, consumer expectations, oil price, GDP, money supply and inflation are statistically significant in explaining stock return within industrial goods and services whilst SEK/USD exchange rate, SEK/EUR exchange rate, oil price, GDP, money supply and inflation are statistically significant in explaining stock return within the banking industry. The analysis of the extent of the impact of these variables on stock return is, however, deemed inconclusive due to time dependencies amongst the variables.Makroekonomi utgör en central del av fundamental analys av aktiemarknaden och följaktligen är förhållandet mellan makroekonomiska variabler och aktiemarknaden snarare väl etablerat än ifrågasatt. Emellertid saknas rådande konsensus om såväl omfattningen av detta förhållande som huruvida förhållandet skiljer sig mellan olika branscher. Syftet med detta kandidatexamensarbete är därför att fastställa de makroekonomiska variabler som är mest avgörande för variationen i aktieavkastning inom två skilda branscher samt andelen av dessa variationer som kan hänföras till just makroekonomiska variabler. I detta syfte tillämpas en multipel linjär regression och två Nasdaqindex - OMX Stockholm Industrial Goods & Services samt OMX Stockholm Banks - tillåts approximera de två utvalda branscherna. Det slutgiltiga resultatet av denna analys är att variablerna reporänta, växelkurs SEK/EUR, konsumentförväntningar, oljepris, BNP, penningmängd och inflation är statistiskt signifikanta för aktieavkastning inom industrin, medan växelkurs SEK/EUR samt SEK/USD, oljepris, BNP, penningmängd och inflation är statistiskt signifikanta för aktieavkastning inom bankbranschen. Analysen av omfattningen av dessa makroekonomiska variablers påverkan på aktieavkastning når emellertid ingen slutsats, vilket anses kunna hänföras till tidsberoende hos variablerna.
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Fridgeirsdottir, Gudrun A. "The development of a multiple linear regression model for aiding formulation development of solid dispersions." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52176/.
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As poor solubility continues to be problem for new chemical entities (NCEs) in medicines development the use and interest in solid dispersions as a formulation-based solution has grown. Solid dispersions, where a drug is typically dispersed in a molecular state within an amorphous water-soluble polymer, present a good strategy to significantly enhance the effective drug solubility and hence bioavailability of drugs. The main drawback of this formulation strategy is the inherent instability of the amorphous form. With the right choice of polymer and manufacturing method, sufficient stability can be accomplished. However, finding the right combination of carrier and manufacturing method can be challenging, being labour, time and material costly. Therefore, a knowledge based support tool based upon a statistically significant data set to help with the formulation process would be of great value in the pharmaceutical industry. Here, 60 solid dispersion formulations were produced using ten, poorly soluble, chemically diverse APIs, three commonly used polymers and two manufacturing methods (spray drying and hot-melt extrusion). A long term stability study, up to one year, was performed on all formulations at accelerated conditions. Samples were regularly checked for the onset of crystallisation during the period, using mainly, polarised light microscopy. The stability data showed a large variance in stability between, methods, polymers and APIs. No obvious trends could be observed. Using statistical modelling, the experimental data in combination with calculated and predicted physicochemical properties of the APIs, several multiple linear regression (MLR) models were built. These had a good adjusted R2 and most showed good predictability in leave-one-out cross validations. Additionally, a validation on half of the models (eg. those based on spray-drying models) using an external dataset showed excellent predictability, with the correct ranking of formulations and accurate prediction of stability. In conclusion, this work has provided important insight into the complex correlations between the physical stability of amorphous solid dispersions and factors such as manufacturing method, carrier and properties of the API. Due to the expansive number of formulations studied here, which is far greater than previously published in the literature in a single study, more general conclusions can be drawn about these correlations than has previously been possible. This thesis has shown the potential of using well-founded statistical models in the formulation development of solid dispersion and given more insight into the complexity of these systems and how stability of these is dependent on multiple factors.
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Buzatoiu, Roxana. "Long Term Forecasting of Industrial Electricity Consumption Data With GRU, LSTM and Multiple Linear Regression." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289632.
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Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Linear Regression (MLR) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Further on, the investigation focuses on tailoring the Recurrent Neural Network model to improve the performance. The experiments focus on the impact of different model architectures. Secondly, it focuses on testing the effect of time-related feature selection as an additional input to the Recurrent Neural Network (RNN) networks. Specifically, it explored how traditional methods such as Exploratory Data Analysis, Autocorrelation, and Partial Autocorrelation Functions Plots can contribute to the performance of RNN model. The current work shows through an empirical study on three industrial datasets that GRU architecture is a powerful method for the long-term forecasting task which outperforms LSTM on certain scenarios. In comparison to the MLR model, the RNN achieved a reduction in the RMSE between 5% up to to 10%. The most important findings include: (i) GRU architecture outperforms LSTM on industrial energy consumption datasets when compared against a lower number of hidden units. Also, GRU outperforms LSTM on certain datasets, regardless of the choice units number; (ii) RNN variants yield a better accuracy than statistical or regression models; (iii) using ACF and PACF as dicovery tools in the feature selection process is unconclusive and unefficient when aiming for a general model; (iv) using deterministic features (such as day of the year, day of the month) has limited effects on improving the deep learning model’s performance.Noggranna långsiktiga energiprognosprognoser för industriella enheter är av intresse för distributionsföretag eftersom det potentiellt kan bidra till att minska deras churn och erbjuda stöd i beslutsfattandet vid säkring. Detta avhandlingsarbete presenterar olika metoder för att prognostisera energiförbrukningen för industriella enheter under en lång tids förutsägelsehorisont på 1 år. I synnerhet inkluderar det experiment med två varianter av de återkommande neurala nätverken, nämligen GRU och LSTM. Deras prestanda jämförs med traditionella metoder, nämligen MLR och SARIMA. Vidare fokuserar undersökningen på att skräddarsy modellen för återkommande neurala nätverk för att förbättra prestanda. Experimenten fokuserar på effekterna av olika modellarkitekturer. För det andra fokuserar den på att testa effekten av tidsrelaterat funktionsval som en extra ingång till RNN -nätverk. Specifikt undersökte den hur traditionella metoder som Exploratory Data Analysis, Autocorrelation och Partial Autocorrelation Funtions Plots kan bidra till prestanda för RNN -modellen. Det aktuella arbetet visar genom en empirisk studie av tre industriella datamängder att GRU -arkitektur är en kraftfull metod för den långsiktiga prognosuppgiften som överträffar ac LSTM på vissa scenarier. Jämfört med MLR -modellen uppnådde RNN en minskning av RMSE mellan 5 % upp till 10 %. De viktigaste resultaten inkluderar: (i) GRU -arkitekturen överträffar LSTM på datauppsättningar för industriell energiförbrukning jämfört med ett lägre antal dolda enheter. GRU överträffar också LSTM på vissa datauppsättningar, oavsett antalet valenheter; (ii) RNN -varianter ger bättre noggrannhet än statistiska modeller eller regressionsmodeller; (iii) att använda ACF och PACF som verktyg för upptäckt i funktionsvalsprocessen är otydligt och ineffektivt när man siktar på en allmän modell; (iv) att använda deterministiska funktioner (t.ex. årets dag, månadsdagen) har begränsade effekter på att förbättra djupinlärningsmodellens prestanda.
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Habgood, Helen Leslie. "Estimation of browse biomass production of Salix SPP. and Betula blandulosa using multiple linear regression." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/24676.
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Browse biomass production of Salix spp. and Betula glandulosa on a wetland in central British Columbia is estimated. Based on an extensive review of much of the literature pertaining to shrub biomass and shrub density estimation, a technique combining regression estimates of average stem biomass with a density estimate obtained using the corrected point distance method was applied. It was found that the best regression relationships were obtained using natural logarithmic transformations of the dimension and biomass variables. It was possible to obtain acceptable biomass equations for the four Salix species encountered without differentiating between the species. More accurate predictions of biomass were achieved using site specific equations and equations based on pooled site data than with general equations. It was concluded that the value of the approach taken is limited if site specific equations are required because of the considerable time required for sample collection and preparation.Forestry, Faculty of
Graduate
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Rowe, Daniel Taylor. "Using Graphics, Animations, and Data-Driven Animations to Teach the Principles of Simple Linear Regression to Graduate Students." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/6.
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This report describes the design, development, and evaluation of the Simple Linear Regression Lesson (SLRL), a web-based lesson that uses visual strategies to teach graduate students the principles of simple linear regression. The report includes a literature review on the use of graphics, animations, and data-driven animations in statistics pedagogy and instruction in general. The literature review also summarizes the pertinent instructional design and development theories that informed the creation of the lesson. Following the literature review is a description the SLRL and the methodologies used to develop it. The evaluation section of the report details the methods used during the formative and summative evaluation stages, including results from a small-group implementation of the SLRL. The report concludes with a review of the product's strengths and weaknesses and the process' strengths and weaknesses.
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Goicoechea, Saioa, and Patricia López. "Modeling the air change rate in a naturally ventilated historical church : MultipleLinear Regression analysis." Thesis, Högskolan i Gävle, Akademin för teknik och miljö, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13640.
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In this thesis the air infiltration through the envelope of a naturally ventilated stone church located in Bergby (Gävle, Sweden) is studied. The project is focused on Multiple Linear Regression (MLR) modeling the air change rate (ACH) inside the church hall and studying the factors (stack effect and wind effect) that influence the air infiltration. The weather parameters outside the building were recorded in a weather station and the properties of the air inside the church was analyzed with different methods. Infrared thermography techniques and thermistors were used to measure the temperature inside, the tracer gas method to measure the ACH and the blower door technique to measure the tightness of the building envelope. In order to know the pressure coefficients on the church envelope a physical model of the building was studied in a wind tunnel. Firstly, only the values obtained from the weather station were used to calculate the predictors of ACH and see which parameter influence more on its variation: temperature difference (∆T) indicating the stack effect; and wind speed (WS), the component of wind speed perpendicular to the long-side facades of the church (WS90) and their square values (WS2 and WS902) indicating the wind effect. The data obtained in the wind tunnel were later used to do the MLR study with new predictors for indicating wind effect (∆Cp∙WS, ∆Cp∙WS2, ∆CpOUT-IN·A∙WS, ∆CpOUT-IN·A∙WS2, ∆CpC-H∙WS, ∆CpC-H∙WS2). Better prediction of ACH was obtained with the square of the wind speed (WS2) instead of the magnitude itself (WS). However, the latter (WS) provided better results than the regression with the magnitude of the perpendicular component of the wind (WS90). Although wind speed influences in ACH, it alone seems to be a very poor predictor of ACH since has a negative correlation with ΔT when the data under study include both day and night. However when high wind speed are detected it has quite strong influence. The most significant predictions of ACR were attained with the combined predictors ∆T & WS and ∆T & ∆CpOUT-IN·A∙WS2. The main conclusion taken from the MLR analysis is that the stack effect is the most significant factor influencing the ACH inside the church hall. This leads to suggest that an effective way of reducing ACH could be sealing the floor and ceiling of the church because from those areas the air infiltration has big influence on the ACH inside the church hall, and more in this case that have been noted that the floor is very leaky. Although different assumptions have been done during the analyses that contribute to make the predictions deviate from reality, at the end it would be possible to asses that MLR can be a useful tool for analyzing the relative importance of the driving forces for ACR in churches and similar buildings, as long as the included predictors not are too mutually correlated, and that attained models that are statistically significant also are physically realistic.Church project
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Patikorn, Thanaporn. "Improvements on Trained Across Multiple Experiments (TAME), a New Method for Treatment Effect Detection." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/792.
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One of my previous works introduced a new data mining technique to analyze multiple experiments called TAME: Trained Across Multiple Experiments. TAME detects treatment effects of a randomized controlled experiment by utilizing data from outside of the experiment of interest. TAME with linear regression showed promising result; in all simulated scenarios, TAME was at least as good as a standard method, ANOVA, and was significantly better than ANOVA in certain scenarios. In this work, I further investigated and improved TAME by altering how TAME assembles data and creates subject models. I found that mean-centering “prior� data and treating each experiment as equally important allow TAME to detect treatment effects better. In addition, we did not find Random Forest to be compatible with TAME.
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Högbom, Johannes, and August Regnell. "Analysis of Performance Measures affecting the economic success on the PGA Tour using multiple linear regression." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275678.
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This bachelor thesis examined the relationship between performance measures and prize money earnings on the PGA Tour. Using regression analysis and data from seasons 2004 through 2019 retrieved from the PGA Tour website this thesis examined if prize money could be predicted. Starting with 102 covariates, comprehensibly covering all aspects of the game, the model was reduced to 13 with Driving Distance being most prominent, favouring simplicity resulting in an R2Adjusted of 0.6918. The final model was discussed in regard to relevance, reliability and usability. This thesis further analysed how the entry of ShotLink, the technology responsible for the vast statistical database surrounding the PGA Tour, have affected golf in general and the PGA Tour in particular. Analysis regarding how ShotLink affected golf on different levels, both for players as well as other stakeholders, where conducted. These show developments on multiple levels; on how statistics are used, golf related technologies, broadcasts, betting market, and both amateur and PGA Tour playing golf players. The analysis of the latter, using statistics from the PGA Tour website, showed a significant improvement in scoring average since ShotLinks inception.Detta kandidatexamensarbete undersökte relationen mellan prestationsmått och prispengar på PGA Touren. Genom regressionsanalys och data från säsongerna 2004 till och med 2019 hämtat från PGA Tourens hemsida undersökte detta arbete om prispengar kunde predikteras. Startandes med 102 kovariat, täckandes alla aspekter av spelet, reducerades sedan modellen till 13 med Utslags Distans mest framträdande, i förmån för simplicitet och resulterande i ett R2Adj på 0.6918. Den slutliga modellen diskuterades sedan gällande relevans, reliabilitet och användbarhet. Vidare analyserar detta arbete hur ShotLinks entré, tekniken ansvarig för den omfattande statistikdatabasen som omger PGA Touren, har påverkat golf generellt och PGA Touren specifikt. Analyser gällande hur ShotLink har påverkat golf på olika nivåer, både för spelare och andra intressenter, genomfördes. Dessa visar utvecklingar på flera fronter; hur statistik används, golfrelaterade teknologier, mediasändningar, bettingmarknad samt både för amatörspelare och spelare på PGA Touren. Den senare analysen, genom användande av statistik från PGA Tourens hemsida, visade på en signifikant förbättring i genomsnittsscore sedan ShotLink infördes.
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Wijkström, Oscar, and Sofia Öhman. "Macroeconomic factors that correlate with the performance of IndustrialTransportation Companies : A study using multiple linear regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209792.
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This thesis in Applied Mathematics and Industrial Economics examines which macroeconomic factors, related to the business cycle, that correlate with the performance of Industrial Transportation Companies. The data for the thesis is collected with the help of Nordea and from reports of each variable. The observations stretch from January 2007 to December 2016, a ten-year period. The data is monthly, hence there are 120 observed data points for each variable. A linear regression analysis was performed with the result that there is a linear relationship between the performance of the Industrial Transportation Companies and the variables Price per MWh, Fuel Price, Exchange rate USD-SEK, Exchange rate EUR-SEK, Manpower Employment Outlook Survey, Repo Rate, OECD Index Sweden, and Purchasing Manager’s Index. Further analysis of the Swedish and international economic history in the last decade was conducted which concluded why said variables were significant.Det här kandidatexamensarbetet i Tillämpad Matematik och Industriell Ekonomi undersöker vilka konjunkturcykelsrelaterade makroekonomiska faktorer som korrelerar med utvecklingen av Industriella Transportmedelsföretag. Datan för arbetet är sammanställd med hjälp av Nordea och från olika rapporter av vardera variabel. Observationerna sträcker sig från Januari 2007 till December 2016, en tioårsperiod. Datan är månatlig, vilket betyder att 120 datapunkter är observerade för vardera variabel. En linjär regressionsanalys tillämpades med resultatet att det finns ett linjärt samband mellan utvecklingen av industriella transportmedelsföretag och kovariaterna Pris per MWh, Bensinpris, Valutakurs USD-SEK, Valutakurs EUR-SEK, Manpower Arbetsmarknadsbarometer, Reporänta, OECD Index Sweden och Inköpschefsindex. Vidare analys av den svenska och internationella ekonomiska historian för det senaste årtiondet genomfördes vilken drog slutsatsen kring varför de nämnda variablerna var signifikanta.
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Brandner, Hanna. "Idenitfying the Influential Factors of the Temporal Variation of Water Consumption : A Case Study using Multiple Linear Regression Analysis." Thesis, KTH, Vattendragsteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192650.
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This thesis is a part of the water development project conducted by Svenskt Vatten, which is the Swedish Water and Wastewater Association (SWWA) as well as Tyréns, a consultancy company with offices based in Stockholm, Sweden. Prior to this thesis work, a quality assessment was conducted for some of the locations provided by municipalities in Sweden. This thesis builds upon the revised water consumption data, and also continues to work with validating and modifying the water measurement data in order to proceed with the next step of the water development project, which is to identify any trends in the temporal variation of water consumption. The main objective of this thesis work is to investigate the influence of climatic, time-related and categorical factors on water consumption data collected for different regions in Sweden, and includes a number of different sectors such as residential, industrial and agricultural water user sectors. For the analysis of data, spectral analysis and sinusoidal modelling will be applied in order to find the periodicity of the data, and then simulate the fitted sinusoidal equation to the observed water consumption data for the hourly interval period. Multiple linear regression analysis is then used to assess what independent variables such as climate, time-related and categorical variables can explain the variation in water consumption over hourly and daily periods of time. Spectral analysis identifies high peaks in the spectral density of the data at 12 and 24 hour cycles, for the hourly water consumption data. For the total daily consumption of water, there is a peak at 7 days, which clarifies that there is a weekly pattern occurring throughout the year. The results from the simple linear regression analysis, where the linear relationship between temperature and water consumption was determined, reveals that the water consumption tends to increase within an increasing temperature, where in Lönashult, Alvesta municipality the water demand increased by 5.5% with every 2 ºC rise in temperature, at a threshold of 12 ºC. For Kalix municipality the three areas selected have around 1-2 % increase in water demand with every 2 ºC rise in temperature for the period of May to December. In Gothenburg, areas that were mixed villa areas or areas with summer homes there was a rise of around 2-12 % in water demand, however areas that are situated in the inner city Gothenburg, or that have majority student housing, the water consumption tends to decrease by 2-7% in water demand with every 2 ºC rise in temperature, with a threshold of 12 ºC. In multiple regression analysis, the hourly water consumption results in adjusted R2 values were in the range from 0.58 to 0.87 (58-87%) for the best model approach and therefore has a significant relationship between water consumption and the explanatory variables chosen for this study. For the daily water consumption, the adjusted R2 values were in the range of 0.22-0.83 (22-83%). The adjusted R2 values are lower for certain areas and can be explained by a number of factors, such as the different variables used for the daily water consumption analysis, as variables that explain more the periodicity of the data such as the sinusoidal fitted variable and hourly or night/day changes in consumption are not included. As well as this, not all independent variables such as the climate variables were available or complete for particular time periods, and also errors in the data can lead to a significantly lower R2 value.
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Prudencio, Gerald, Diego Pino, Luis Arauzo, and Carlos Raymundo. "Productivity analysis of LHD equipment using the multiple linear regression method in an underground mine in Peru." International Institute of Informatics and Systemics, IIIS, 2019. http://hdl.handle.net/10757/656294.
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.The current study is based on a multiple linear regression analysis with an objective to formulate an equation related to the productivity analysis of LHD equipment using independent variables such as the effective utilization of the equipment. To identify the independent variables, main productive factors, such as the actual capacity of the buckets, the transport cycles in the cleaning process, and the performance by means of curves, were analyzed. Comparisons of a Peruvian underground mine case study exhibited that the battery-powered equipment denoted similar production efficiencies to that exhibited by its diesel counterparts; however, the three-tier approach observed that the battery-powered equipment could achieve production efficiencies that are up to 13.8% more as compared to that achieved using its diesel counterparts because of increased effective utilization that can be attributed to long MTBF. The results of this study exhibit that LHDs under battery-powered storage are feasible for underground mining not only because of the fact that they do not emit any polluting gases, which helps to mitigate pollution, but also because of their good production performance that can be considered to be an important pillar in deep mining. Copyright 2019.
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Uys, Daniel Wilhelm. "Influential data cases when the C-p criterion is used for variable selection in multiple linear regression." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53464.
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Dissertation (PhD)--Stellenbosch University, 2003.ENGLISH ABSTRACT: In this dissertation we study the influence of data cases when the Cp criterion of Mallows (1973) is used for variable selection in multiple linear regression. The influence is investigated in terms of the predictive power and the predictor variables included in the resulting model when variable selection is applied. In particular, we focus on the importance of identifying and dealing with these so called selection influential data cases before model selection and fitting are performed. For this purpose we develop two new selection influence measures, both based on the Cp criterion. The first measure is specifically developed to identify individual selection influential data cases, whereas the second identifies subsets of selection influential data cases. The success with which these influence measures identify selection influential data cases, is evaluated in example data sets and in simulation. All results are derived in the coordinate free context, with special application in multiple linear regression.
AFRIKAANSE OPSOMMING: Invloedryke waarnemings as die C-p kriterium vir veranderlike seleksie in meervoudigelineêre regressie gebruik word: In hierdie proefskrif ondersoek ons die invloed van waarnemings as die Cp kriterium van Mallows (1973) vir veranderlike seleksie in meervoudige lineêre regressie gebruik word. Die invloed van waarnemings op die voorspellingskrag en die onafhanklike veranderlikes wat ingesluit word in die finale geselekteerde model, word ondersoek. In besonder fokus ons op die belangrikheid van identifisering van en handeling met sogenaamde seleksie invloedryke waarnemings voordat model seleksie en passing gedoen word. Vir hierdie doel word twee nuwe invloedsmaatstawwe, albei gebaseer op die Cp kriterium, ontwikkel. Die eerste maatstaf is spesifiek ontwikkelom die invloed van individuele waarnemings te meet, terwyl die tweede die invloed van deelversamelings van waarnemings op die seleksie proses meet. Die sukses waarmee hierdie invloedsmaatstawwe seleksie invloedryke waarnemings identifiseer word beoordeel in voorbeeld datastelle en in simulasie. Alle resultate word afgelei binne die koërdinaatvrye konteks, met spesiale toepassing in meervoudige lineêre regressie.
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Olaya, Bucaro Orlando. "Predicting risk of cyberbullying victimization using lasso regression." Thesis, Uppsala universitet, Statistiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.
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The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation variables, school performance variables, physical health variables, mental health variables and health risk variables such as alcohol and drug consumption. Included variables in the final model are demographic variables, mental health variables and parental mediation variables. Variables excluded in the final model includes dietary habit variables, school performance variables, physical health variables and health risk variables. The final model has an overall prediction accuracy of 88%.
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Leda, Victor Costa [UNESP]. "Modelagem da produtividade de cana-de-açúcar utilizando índices de vegetação." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/143945.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A produção da cana-de-açúcar é destaque no cenário econômico do estado São Paulo, dessa forma confirma-se a necessidade do monitoramento dessa cultura, de maneira a contribuir com melhorias em decisões e planejamentos operacionais. A produção total e a produtividade da cana-de-açúcar são fatores de grande interesse para os agricultores, pois é a partir dessa informação que a programação das operações são realizadas, porém, essas estimativas não possuem métodos de alta precisão e confiança em amostragens não destrutivas. O homem possui excelente capacidade de analisar e interpretar resultados, mas também está sujeito a subjetividades em suas avaliações. A análise empreendida no trabalho teve como objetivo a elaboração de modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, possui aproximadamente 6000 hectares, com altimetrias variando entre 600 e 700 metros. Para a coleta das informações espectrais, utilizou-se as imagens do satélite Landsat 8, com órbita/ponto em 221/076. Nos resultados do trabalho realizado, constatou-se que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97. Sendo que em períodos com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto que em períodos com coeficientes de determinação baixos, muito provavelmente foram obtidos devido a outros fatores listados terem ocorrido como dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.
The production of sugarcane is a highlight in the economic scenario in the state of São Paulo, thus it confirms the need of monitoring this culture, in order to contribute to improvements in making decisions and operational planning.The production and productivity of sugarcane are factors of great interest to farmers, because, from this information the planning of operations is performed out, however, these estimates do not have high precision and reliable methods for non-destructive sampling.The human has an excellent ability to analyze and interpret results, but may also be affected by the subjectivity of their evaluations.The analysis undertaken in this work aimed at the development of mathematical models to explain the productivity of sugarcane through geoprocessing and remote sensing.The experiment was conducted in commercial area of Agrícola Rio Claro, partner of Zilor group, which is located in Lençóis Paulista and Pratânia, of approximately 6000 hectares, with altimetry ranging between 600 and 700 meters. For the collection of the spectral information, it was used the images of the satellite Landsat 8, with orbit/point 221/076. The results of the work, it was found that all the modeling were satisfactory, varying the coefficient of determination between 0.15 to 0.97. Given that, in periods with high coefficients of determination areas may be generally found in clusters, suggesting a lower incidence of variables. While in periods of low coefficient of determination, it was most likely obtained due to other factors listed of having occurred such as a dispersion of the plots in the area, soil types, rainfall and varieties, probably distinctly.
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Flynn, Myles M. 1966. "A method of assessing near-view scenic beauty models: A comparison of neural networks and multiple linear regression." Thesis, The University of Arizona, 1997. http://hdl.handle.net/10150/292054.
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With recent advances in artificial intelligence, new methods are being developed that provide faster, and more consistent predictions for data in complex environments. In the field of landscape assessment, where an array of physical variables effect environmental perception, natural resource managers need tools to assist them in isolating the significant predictors critical for the protection and management of these resources. Recent studies that have utilized neural networks to assist in developing predictive models of scenic beauty that have typically utilized linear regression techniques have found limited success. The goal of this research is to compare NN's with linear regression models to determine their efficiency predictive capability for assessing near view scenic beauty in the Cedar City District of the Dixie National forest (DNF). Results of this study strongly conclude that neural networks are consistently better predictors of near view scenic beauty in spruce/fir dominated forests than hierarchical linear regression models.
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Book, Emil, and Linus Ekelöf. "A Multiple Linear Regression Model To Assess The Effects of Macroeconomic Factors On Small and Medium-Sized Enterprises." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254298.
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Small and medium-sized enterprises (SMEs) have long been considered the backbone in any country’s economy for their contribution to growth and prosperity. It is therefore of great importance that the government and legislators adopt policies that optimise the success of SMEs. Recent concerns of an impending recession has made this topic even more relevant since small companies will have greater difficulty withstanding such an event. This thesis will focus on the effects of macroeconomic factors on SMEs in Sweden, with the usage of multiple linear regression. Data was collected for a 10 year period, from 2009 to 2019 at a monthly interval. The end result was a five variable model with an coefficient of determination of 98%.Små- och medelstora företag (SMEs) har länge varit ansedda som en av de viktigaste komponenterna i ett lands ekonomi, främst för deras bidrag till tillväxt och framgång. Det är därför mycket viktigt att regeringar och lagstiftare för en politik som främjar SMEs optimala tillväxt. Flera år av högkonjunktur och oro över kommande lågkonjunktur har gjort detta ämne ytterst relevant då små företag är de som kommer att drabbas värst av en svårare ekonomisk tillvaro. Denna rapport använder multipel linjär regression för att utvärdera effekterna av olika makroekonomiska faktorer på SMEs i Sverige. Data har insamlats månadsvis för en 10 årsperiod mellan 2009 till 2010. Resultatet blev en modell med fem variabler och en förklaringsgrad på 98%.
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Löwe, Rakel, and Ida Schneider. "Automatic Differential Diagnosis Model of Patients with Parkinsonian Syndrome : A model using multiple linear regression and classification tree learning." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413638.
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Parkinsonian syndrome is an umbrella term including several diseases with similar symptoms. PET images are key when differential diagnosing patients with parkinsonsian syndrome. In this work two automatic diagnosing models are developed and evaluated, with PET images as input, and a diagnosis as output. The two devoloped models are evaluated based on performance, in terms of sensitivity, specificity and misclassification error. The models consists of 1) regression model and 2) either a decision tree or a random forest. Two coefficients, alpha and beta, are introduced to train and test the models. The coefficients are the output from the regression model. They are calculated with multiple linear regression, with the patient images as dependent variables, and mean images of four patient groups as explanatory variables. The coefficients are the underlying relationship between the two. The four patient groups consisted of 18 healthy controls, 21 patients with Parkinson's disease, 17 patients with dementia with Lewi bodies and 15 patients with vascular parkinsonism. The models predict the patients with misclassification errors of 27% for the decision tree and 34% for the random forest. The patient group which is easiest to classify according to both models is healthy controls. The patient group which is hardest to classify is vascular parkinsonism. These results implies that alpha and beta are interesting outcomes from PET scans, and could, after further development of the model, be used as a guide when diagnosing in the models developed.
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Januario, Ana Paula Ferrari. "Análise estatística da produção de vitelão Mertolengo." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29316.
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The work was intended to support the Association of mertolenga cattle breed in its breeding process and
decision making, namely in modeling the cost per day of production of the male mertolenga cattle, and
in identifying the variables that favor the sale of the animal as a product with a protected designation of
origin (PDO) seal. The database contained information on 716 male animals, of which 54 % went to the
slaughter that guarantees the PDO seal. We also had data on the cost structure production of the animals
from when it enters into the CTR to slaughter, in addition to the individual characteristics of each animal,
in particular, of its estimated breeding value.
To obtain the cost-per-day production model, multiple linear regression models and other generalized
linear models were used. For the classi cation of the animal as a PDO slaughter destination, a logistic
regression model was used.
When we comparing the generalized linear models tested, the multiple linear regression model was
con rmed as the best technique to explain the cost per day of production. For this model, it was found
that information such as weight at entry as well as di erent estimated breeding value positively in
uence
the cost of production.
With regard to logistic regression, weight at entry, age at entry and genetic values referring to
maternal capacity and calving interval are factors that enhance the animal being sold under the PDO seal; Sumário:
Com o trabalho desenvolvido nesta dissertação, pretendeu-se apoiar a Associação de produtores de bovinos
da raça mertolenga no seu processo de recria e nas tomadas de decisão, nomeadamente na modelação do
custo por dia de produção de bovinos machos da raça mertolenga, e na identificação das variáveis que
favorecem a venda do animal como um produto com selo de denominação de origem protegida (DOP).
A base de dados continha a informação de 716 animais machos, dos quais 54% foram para o abate que
garante o selo DOP, dados referentes _a estrutura de custo de produção dos animais desde a entrada no CTR
até o abate, além das características individuais de cada animal, em particular, dos seus valores genéticos.
Para obter o modelo do custo por dia de produção, utilizou-se modelos de regressão linear múltipla
e outros modelos lineares generalizados. Para a classificação do animal por destino de abate DOP, utilizou-se
um modelo de regressão logística. Quando se comparou os diferentes modelos lineares generalizados
testados, confirmou-se o modelo de regressão linear multipla como o mais adequado para explicar o custo
por dia de produção. Para este modelo, verificou-se que informações como o peso à entrada bem como
diferentes valores genéticos infuenciam de forma positiva o custo de produção.
No que diz respeito a regressão logística, o peso à entrada, a idade à entrada e os valores genéticos
referentes à capacidade maternal e intervalo entre partos são fatores potenciadores do animal ser vendido
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Veloso, Ricardo Campos. "Modelagem de curvas de degradação de correias transportadoras com base em covariáveis inerentes ao processo de mineração." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/127780.
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Esta tese tem como objetivo a modelagem da degradação de correias em transportadores utilizados em mineração, como função do tempo e de outras covariáveis independentes que fazem parte do processo de mineração e que influenciam no desgaste das mesmas. Para a realização do trabalho, utilizou-se um método dividido em duas etapas: (i) abordagem qualitativa (estudo teórico do tópico degradação de correias e coleta de dados através da técnica de Grupo Focado – GF), para definição de variáveis influentes no desgaste, e (ii) abordagem quantitativa, para obtenção do modelo de degradação das correias, sendo utilizada, no estudo em questão, uma regressão linear múltipla. Como resultado foi possível identificar através da literatura, assim como via GF, que as variáveis ciclo da correia, comprimento e largura da correia, queda do material, limpador de correias (raspadores), taxa de alimentação, granulometria, composto e velocidade da correia impactariam potencialmente na degradação de correias. Já com o uso da regressão múltipla, constatou-se que as mesmas realmente são significativas e influentes, corroborando os dados obtidos via GF. De posse dos modelos de degradação obtidos para cada correia, foi possível elaborar uma proposta de sistemática de gestão da degradação de correias, baseada na comparação da evolução do desgaste real com o previsto, de modo a se detectar possíveis desvios e permitir a elaboração de ações de correção, visando minimizar a degradação acelerada e maximizar a vida útil das correias. Conseguiu-se estimar um ganho financeiro potencial de cerca de R$ 1.132.000,00 por ano, a partir da comparação entre a vida útil calculada pelos modelos de degradação e a vida estimada pela área de manutenção do complexo.This thesis aims at modelling of the conveyor’s belt degradation used in mining as a function of time and other independent covariate that are part of the mining process and have influence in their wearing. To carry out the research we implemented a method divided in two stages: (i) a qualitative approach (theoretical study of conveyor belts degradation and data collection through Focused Groups – FG) for definition of factors that are influential in the wearing of belts, and (ii) a quantitative approach for obtaining a belts’ degradation model through multiple linear regression. It was possible to identify in the literature and through FG that variables such as belt cycle, belt length and width, material fall, belt cleaner, feed rate, particle size, compound and belt speed could potentially impact on the degradation of belts. Using multiple regression such variables were found to be statistically significant, corroborating the data obtained from FG. With the degradation models obtained for each conveyor belt it was possible to propose a method for the maintenance management of conveyor belts. The method was based on the comparison of real wear versus predicted wear in order to detect possible deviations and to allow the development of correction actions that aim at minimizing accelerated degradation and maximizing the belt’s lifetime. A potential financial gain of approximately R$ 1.132.000,00 per year was estimated comparing the lifetime obtained using the degradation models and the life estimated by the maintenance area of the complex.
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Addo, Evans Dapaa. "Performance Comparison of Imputation Algorithms on Missing at Random Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3422.
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Missing data continues to be an issue not only the field of statistics but in any field, that deals with data. This is due to the fact that almost all the widely accepted and standard statistical software and methods assume complete data for all the variables included in the analysis. As a result, in most studies, statistical power is weakened and parameter estimates are biased, leading to weak conclusions and generalizations.
Many studies have established that multiple imputation methods are effective ways of handling missing data. This paper examines three different imputation methods (predictive mean matching, Bayesian linear regression and linear regression, non Bayesian) in the MICE package in the statistical software, R, to ascertain which of the three imputation methods imputes data that yields parameter estimates closest to the parameter estimates of a complete data given different percentages of missingness. In comparing the parameter estimates of the complete data and the imputed data, the parameter estimates in each model were evaluated and compared. The paper extends the analysis by generating a pseudo data of the original data to establish how the imputation methods perform under varying conditions.
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Zhai, Jing. "Efficient Exact Tests in Linear Mixed Models for Longitudinal Microbiome Studies." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612412.
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Microbiome plays an important role in human health. The analysis of association between microbiome and clinical outcome has become an active direction in biostatistics research. Testing the microbiome effect on clinical phenotypes directly using operational taxonomic unit abundance data is a challenging problem due to the high dimensionality, non-normality and phylogenetic structure of the data. Most of the studies only focus on describing the change of microbe population that occur in patients who have the specific clinical condition. Instead, a statistical strategy utilizing distance-based or similarity-based non-parametric testing, in which a distance or similarity measure is defined between any two microbiome samples, is developed to assess association between microbiome composition and outcomes of interest. Despite the improvements, this test is still not easily interpretable and not able to adjust for potential covariates. A novel approach, kernel-based semi-parametric regression framework, is applied in evaluating the association while controlling the covariates. The framework utilizes a kernel function which is a measure of similarity between samples' microbiome compositions and characterizes the relationship between the microbiome and the outcome of interest. This kernel-based regression model, however, cannot be applied in longitudinal studies since it could not model the correlation between the repeated measurements. We proposed microbiome association exact tests (MAETs) in linear mixed model can deal with longitudinal microbiome data. MAETs can test not only the effect of overall microbiome but also the effect from specific cluster of the OTUs while controlling for others by introducing more random effects in the model. The current methods for multiple variance component testing are based on either asymptotic distribution or parametric bootstrap which require large sample size or high computational cost. The exact (R)LRT tests, an computational efficient and powerful testing methodology, was derived by Crainiceanu. Since the exact (R)LRT can only be used in testing one variance component, we proposed an approach that combines the recent development of exact (R)LRT and a strategy for simplifying linear mixed model with multiple variance components to a single case. The Monte Carlo simulation studies present correctly controlled type I error and provided superior power in testing association between microbiome and outcomes in longitudinal studies. Finally, the MAETs were applied to longitudinal pulmonary microbiome datasets to demonstrate that microbiome composition is associated with lung function and immunological outcomes. We also successfully found two interesting genera Prevotella and Veillonella which are associated with forced vital capacity.
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Guterstam, Rasmus, and Vidar Trojenborg. "Exploring a personal property pricing method in insurance context using multiple regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254300.
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In general, insurance companies and especially their clients face long and complicated claims processes where payments rarely, and almost reluctantly, are made the same day. A part of this slow moving procedure is the fact that in some cases the insurer has to value the personal property themselves, which can be a tedious process. In conjunction with the insurance company Hedvig, this project address this issue by examining a pricing model for a specific personal property; smartphones - one of the most common occurring claim types in the insurance context. Using multiple linear regression with data provided by PriceRunner, 10 key characteristics out of 91 where found to have significant explanatory power in predicting the market price of a smartphone. The model successfully simulates this market price with an explained variance of 90%. Furthermore this thesis illustrates an intuitive example regarding pricing models for personal property of other sorts, identifying limiting key components to be data availability and product complexity.I dagsläget står försäkringsbolag och deras kunder allt för ofta inför långa och komplicerade försäkringsärenden, där utbetalningar i regel aldrig sker samma dag. En del i denna långsamma och utdragna utbetalningsprocess är det faktum att försäkringsbolaget på egen hand måste uppskatta egendomens värde, vilket kan vara en mycket komplicerad process. I samarbete med försäkringsbolaget Hedvig undersöker denna rapport en värderingsmodell för ett av de vanligaste försäkringsärendena gällande personlig egendom, nämligen smartphones. Genom att använda multipel linjär regression med data försedd av PriceRunner har 10 av 91 nyckelfaktorer identifierats ha signifikant förklaringsgrad vid modellering av marknadsvärdet av en smartphone. Den framtagna modellen simulerar framgångsrikt marknadsvärdet med en 90-procentig förklaringsgrad av variansen. Vidare illustrerar denna rapport intuitiva riktlinjer för värderingsmodellering till andra typer av personlig egendom, samtidigt som den identifierar begränsande nyckelaspekter som exempelvis tillgången på data och egendomens inneboende komplexitet.
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Carvalho, Tânia Maria de [UNESP]. "Modelagem digital de atributos de solo da Fazenda Edgárdia - Botucatu-SP." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148704.
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O mapa de solos é uma ferramenta essencial para o planejamento de uso da terra e estudos que envolvem aspectos ambientais relativos a esse importante recurso natural. Técnicas quantitativas e ferramentas de geoprocessamento têm sido aliadas à interpretação dos processos pedogenéticos para possibilitar a elaboração de mapas mais precisos, obtidos por processo mais rápido e menos oneroso. Dentre os modelos aplicados, os denominados modelos híbridos empregam variáveis auxiliares preditoras e autocorrelação espacial, para viabilizar a predição de atributos de solo em locais não amostrados. A iniciativa para mapeamento digital do solo em escala mundial – GlobalSoilMap.net atua no sentido de disponibilizar representações globais de atributos de solo, elaboradas por meio da aplicação de modelo híbrido em dados legados de solos, realizando a prática do Mapeamento Digital de Solos (MDS). Com base nesse princípio, esse trabalho baseou-se na hipótese de que a aplicação da técnica híbrida regressão-krigagem, utilizando dados legados de levantamento de solo e covariáveis de relevo e sensoriamento remoto proveem mapa de atributos de solo representativos de uma área da Cuesta de Botucatu. O modelo foi aplicado localmente, a duas profundidades, para representação contínua do Índice de Avermelhamento (IAV), saturação de bases (V%), teor de areia, teor de argila, CTC e pH dos solos da Fazenda Experimental Edgárdia, para a qual são disponíveis dados de levantamento de solo. As covariáveis preditoras derivadas de um MDE e de imagem orbital foram uniformizadas a uma resolução espacial de 10 m, e os métodos foram selecionados de acordo com a verificação de correlação linear significativa entre atributos e covariáveis e autocorrelação espacial dos atributos ou dos resíduos de regressões lineares múltiplas (RLM). Os dados foram separados em subconjuntos de treinamento e validação. Os coeficientes de correlação entre atributos de solo e covariáveis foram significativos e variaram de -0,40 a 0,51. Os preditores mais correlacionados aos atributos foram Índice Topográfico de Umidade (ITU), Declividade (Decl), Aspecto (Aspc), Elevação (Elev) e índice de vegetação NDVI, sendo os quatro últimos os principais na estimação das frações texturais. Os valores de R² ajustado das RLM, entre 0,10 e 0,36, foram considerados baixos. De modo geral, os mapas de predição expuseram padrões característicos da variação espacial observada nos mapas das covariáveis preditoras, usadas na calibração dos modelos. Foi observado um incremento na acurácia entre as duas etapas do processo de RK, indicando que o mapa final é superior em relação à RLM. No entanto, os modelos apresentaram, de modo geral, um baixo desempenho quando avaliados por meio de validação externa, mesmo com a estratificação em duas áreas mais uniformes em termos de relevo. Os resultados indicaram a limitação do uso de amostragem para fins de levantamento em modelos de predição. Houve ainda dificuldade de aplicação dos modelos em função do contexto litológico complexo e da dinâmica local de formação de solos, que não puderam ser detectadas pelas covariáveis selecionadas. Apesar das limitações, os mapas de predição apresentaram coerência com o conhecimento relativo aos atributos, nas condições locais.
The soil map is an essential tool for land use planning and studies related to environmental aspects of this important natural resource. Quantitative techniques and geoprocessing tools are currently combined with the interpretation of pedogenic processes to enable the development of more accurate maps obtained by faster and less costly process. Among the models applied to it, the hybrid models employ predictive auxiliary variables and spatial autocorrelation, to enable the prediction of soil attributes in unsampled locations. The digital soil mapping worldwide project – GlobalSoilMap.net acts in order to provide global representations of soil attributes developed through the application of hybrid model in legacy soil data, performing the practice of Digital Soil Mapping (MDS). This work was based on the assumption that the application of the hybrid technique of regression-kriging (RK), using legacy data of soil survey and covariates of relief and remote sensing provide representative map of soil attributes of an area in Cuesta of Botucatu. The goal was to apply locally, in two depths, prediction models and continuous representation of Soil Redness Index (IAV), base saturation index (V%), sand content and clay content, cation-exchange capacity (CTC) and pH of the soils in Edgardia Experimental Farm, for which are available soil survey data. The predictor covariates were derived from an Digital Elevation Model (MDE) and an orbital image. They were all standardized at spatial resolution of 10 m, the methods were selected by checking significant linear correlation between attributes and covariates and spatial autocorrelation of attributes or residues of multiple linear regressions (RLM). The data were separated into training and validation subsets. The correlation coefficients (r) between soil attributes and covariates were significant and ranged from -0.40 to 0.51. The predictors more correlated to attributes were topographic wetness index (ITU), slope (Decl), aspect (Aspc), elevation (Elev) and vegetation index (NDVI), and the last four are key definers of granulometric fractions. The values of adjusted R² of RLM were between 0.10 and 0.36, which is considered low. In general, the prediction maps exhibited characteristic patterns of spatial variation observed in the covariates maps, used in the calibration of the models. An increase in accuracy was observed between the two steps of the modeling process by RK, indicating that the final map is better than the RLM. However, the models showed generally low performance, and did not provide good results when evaluated by external validation and even if the area was stratified in two smaller plots, with more homogeneous relief. The results indicated the restricted use of soil survey sampling in prediction models, and the difficulty of applying MDS in areas with complex lithology, especially where the correlation between local dynamics of soil genesis and selected covariates are not strong. Despite the limitations, the prediction maps were consistent with knowledge about soil properties in local conditions.
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com, emmayuen@hotmail, and Emma Yuen. "Water Consumption Patterns in Australian Aboriginal Communities." Murdoch University, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20051119.134422.
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Aboriginal Australians have a significantly lower health status than their non-Aboriginal counterparts. To facilitate healthy living practices necessary for good health, a high level investment is currently made in water services, on the assumption that there is a relationship between the volume and quality of water supplied with health outcomes, despite the high economic and environmental cost. This thesis investigates whether the current design supply criteria of 1000-1200 litres per person per day of water, meeting the Australian Drinking Water Quality Guidelines, is both sufficient and necessary to improve the health of Aboriginal Australians. The scope of the thesis is limited to the sufficiency of design guidelines although it necessarily also touches on the broader issues of Aboriginal health.
Both qualitative and quantitative methods were used to explore current water consumption patterns of consumers at multiple hierarchical levels (community, household and individual) and hence the requirements of physical infrastructure on which consumers depend. Multiple linear regression was used to consider factors correlated with supply volume, while metering was used at both the domestic and appliance level to determine where and how water was used. Meters were installed on fixtures in two houses in a community near Alice Springs. This was then complemented by qualitative information obtained through focus group discussions, key informant interviews and observation in the field. The appropriateness of the supply of high quality water for all uses was addressed by considering the volume of drinking water intake and its impact on the derivation of water quality guidelines. This was achieved by a face-to-face survey involving 57 volunteers. Fieldwork was conducted predominantly in three communities near Alice Springs although some additional data was collected in other communities in Western Australia and the Northern Territory.
The results showed that the factors influencing water consumption were highly complex and variable between communities and individuals. However, there were some culturally specific needs identified in Aboriginal communities, such as the need for temperature and dust control, as well as the reduction of losses. The unique characteristics of each community made it difficult to provide a more precise estimate for design supply. As a result, overly conservative guidelines such as those already used are necessary in the short term despite there being no guarantee of improved health. In the long term, issues of community governance and capacity building will start to be addressed, and the realisation that social systems are both complex and dynamic will need to be reflected in policy. These issues were represented in a systemic conceptual model at the end of the thesis, which also highlighted inadequacies of reductionist approaches such as design supply guidelines. The thesis concluded that complex problem situations such as that of health, require a systems approach.
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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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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