Dissertations / Theses: 'Multiply regression'

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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) Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) 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) Made available in DSpace on 2013-06-03T19:20:55Z (GMT). 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) Previous issue date: 2009 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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Ernstsson, Hampus, and Liljesvan Max Börjes. "Multiples for Valuation Estimates of Life Science Companies in Sweden." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254239.

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Market multiples are a common and simple tool for estimation of corporate value. It can express temporal dynamics and differences in markets, industries and firms. Despite their practical usefulness, some critical problems remains which continue to be debated. This thesis investigates if there exists characteristics for explaining market capitalization by market multiples within the life science industry in Sweden. The approach follows well known theory of multiple linear regression analysis. The results indicated only a linear relationship between the market cap and the R\&D expenditures of a company. This does not mean that the other explanatory variables does not have effect on market cap only that there is no linear relationship that could be statistically proven. Värderingsmultiplar är ett vanligt och enkelt verktyg för att approximera företags värde. Det kan beskriva temporär dynamik och skillnader hos marknader, industrier och bolag. Trots dess praktiska användbarhet finns en del kritiska problem som fortfarande debatteras. Denna uppsats undersöker om det existerar några egenskaper för att förklara marknadsvärdet med hjälp av värderingsmultiplar inom life science industrin i Sverige. Tillvägagångssättet följer välkänd teori om multipel linjär regressions analys. Resultaten visade att det endast finns ett samband mellan marknadsvärdet och utgifter för forskning och utveckling för ett bolag. Detta innebär inte att de andra variablerna inte har någon effekt på marknadsvärdet, utan att det inte finns ett linjärt samband som kan bevisas på ett statistiskt vis.

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Krebs-Brown, Axel Johannes. "Shrinkage and calibration in multiple regression." Thesis, University of Warwick, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364602.

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Azmi, Muhammad Arsalan Raza. "Workers' Compensation Modeling Using Multiple Regression." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28850.

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Workers? compensation insurance (WCI) is the highest cost to an employer following accidents. It is needed to predict the benefits value without taking into account the past records of an employee, which is not readily available in most cases. Employment and workers? compensation data were acquired from the Bureau of Labor Statistics and the National Academy of Social Insurance, respectively. The statistical model was developed with SAS using multiple regression and the process was simplified using analysis of covariance (ANCOVA). The model predicted future values of workers compensation given a known number of covered workers for all U.S. states. The model is statistically proven to be fit for all states. The states were compared on the basis of percentage deviation from the actual values. By using this model, insurance companies and policymakers can have better understanding of workers? compensation trend and they can quotes premiums and develop policies more accurately.

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Hauser, Andrea. "Building a risk map for hurricane-force tropical cyclones in continental Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/23306.

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Mestrado Bolonha em Actuarial Science Tropical cyclones have enormous destructive potential. In 2018 continental Por- tugal has been aﬀected by hurricane Leslie, the weather-related event having the highest impact ever on the property portfolio of the portuguese insurance company Fidelidade, causing several millions euros of losses. The fear is that, in the near future, the occurrence of this type of events increases in intensity and frequency, as a consequence of the climate change due to the warming of the planet. Quantifying the potential loss to which the property portfolio of Fidelidade could be subject to, helps in approximately determining premiums and capital reserves, as well as in deﬁning the coverage to be provided. In this work, an approach to model the costs caused by a tropical cyclone extreme event is presented. The model is based on the losses incurred by the property port- folio of Fidelidade due to hurricane Leslie. By using the estimated models, it is possible to produce cost estimates for diﬀerent scenarios of interest for the com- pany. The estimated models are also used to build a risk map for the councils of continental Portugal. The results obtained indicate that the councils with the estimated higher average cost ratio are all located along the coast of the country. Ciclones tropicais têm um enorme potencial de destruição. Em 2018, Portugal continental foi atingido pelo furacão Leslie, que constituiu o fenómeno metereológico de maior impacto, até à data, no portfolio da companhia de seguros Fidelidade, causando milhões de euros em perdas. De facto, os ciclones tropicais têm um enorme potencial de destruição. A preocupação é que, em breve, a ocorrência deste tipo de fenómenos aumente em intensidade e frequência, como consequência das mudanças climáticas provocadas pelo aquecimento global. Quantiﬁcar a potencial perda à qual a companhia Fidelidade pode estar sujeita ajuda a determinar aproximadamente os prémios e provisões, assim como a deﬁnir a cobertura a ser providenciada. Neste trabalho, é apresentada uma abordagem para modelar os custos causados por um ciclone tropical extremo. O modelo é baseado nas perdas provocadas ao portfolio da Fidelidade pelo furacão Leslie. Ao usar os modelos, é possível produzir custos estimados para diferentes cenários de interesse da companhia. Os modelos estimados sã também utilizados para construir um mapa de risco para os conselhos de Portugal continental. Os resultados obtidos indicam que os conselhos com a maior taxa média de custos estimada estão localizados ao longo da costa do país. info:eu-repo/semantics/publishedVersion

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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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Submitted by TÂNIA MARIA DE CARVALHO null (taniacarvalho2010@gmail.com) on 2017-02-02T19:26:12Z No. of bitstreams: 1 TESE_arquiv.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5) Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-06T16:42:11Z (GMT) No. of bitstreams: 1 carvalho_tm_dr_bot.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5) Made available in DSpace on 2017-02-06T16:42:11Z (GMT). No. of bitstreams: 1 carvalho_tm_dr_bot.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5) Previous issue date: 2016-12-19 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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Nordin, Henrik, and Gustav Klockby. "Bestämningsfaktorer till regionala bostadspriser : En analys av de svenska länen för perioden 1993-2012." Thesis, Linköpings universitet, Nationalekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111160.

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Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämningsfaktorer till de svenska bostadspriserna på länsnivå. Sålunda är ett bidragande mål med denna studie att tillföra en bättre förståelse för dynamiken på den svenska bostadsmarknaden. I studien använder vi multipel regression där vi bearbetar paneldata med en Fixed Effect Model. Ett flertal förtester har gjorts för att få fram den mest tillförlitliga modellen i vilken vi skattat bostadspriserna utifrån teoretiskt belagda förklaringsvariabler. De slutsatser vi har dragit är att disponibel inkomst, befolkningstäthet och sysselsättningsgrad kan förklara bostadspriserna på länsnivå med en procents signifikansnivå. Skillnaden i bostadspriserna mellan länen har relativt sett ökat över tidsperioden för studien. Avslutningsvis diskuteras uppvisade avvikelser mellan de verkliga bostadspriserna och de skattade bostadspriserna vilka kan förklaras av att bostadsmarknaden är känslostyrd med inslag av spekulationer. The housing market is one of the greatest assets markets in a given country. Therefore, changes in housing prices have a big impact on the single household, the financial system and the economic system as a whole. Due to the housing markets vital role in the society, many scientific studies have been done with the purpose of enlighten and discover the dynamics of the Swedish housing market. The focuses in these earlier studies have more than often taken a metropolitan perspective or compared the Swedish housing market with other countries. However, this study divides the Swedish housing market into regional county level with the purpose of analyzing determinants of housing prices due to county specific variables. By analyzing the housing prices due to county specific factors a contributing goal with this study is to deepen the understanding about the dynamics in the Swedish housing market. In this study we have used multiple regressions in order to work with panel data. The Fixed Effect Model fitted our purpose well which is why that kind of model was used in order to estimate the housing prices for every single Swedish county. The conclusions drawn in this study are that disposable income, people density and employment rate are all statistically significant on one percent level in order to explain the housing price at state level. We have also discovered that, during the observed period, the relative differences in housing prices between the different states have increased. Finally, the differences found between the real housing prices and the estimated housing prices, can be explained by the assumption that the housing market is driven by emotions and speculations.

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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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Mhitarean, Ecaterina. "Marketing Mix Modelling from the multiple regression perspective." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208474.

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The optimal allocation of the marketing budget has become a difficult issue that each company is facing. With the appearance of new marketing techniques, such as online advertising and social media advertising, the complexity of data has increased, making this problem even more challenging. Statistical tools for explanatory and predictive modelling have commonly been used to tackle the problem of budget allocation. Marketing Mix Modelling involves the use of a range of statistical methods which are suitable for modelling the variable of interest (in this thesis it is sales) in terms of advertising strategies and external variables, with the aim to construct an optimal combination of marketing strategies that would maximize the profit. The purpose of this thesis is to investigate a number of regression-based model building strategies, with the focus on advanced regularization methods of linear regression, with the analysis of advantages and disadvantages of each method. Several crucial problems that modern marketing mix modelling is facing are discussed in the thesis. These include the choice of the most appropriate functional form that describes the relationship between the set of explanatory variables and the response, modelling the dynamical structure of marketing environment by choosing the optimal decays for each marketing advertising strategy, evaluating the seasonality effects and collinearity of marketing instruments. To efficiently tackle two common challenges when dealing with marketing data, which are multicollinearity and selection of informative variables, regularization methods are exploited. In particular, the performance accuracy of ridge regression, the lasso, the naive elastic net and elastic net is compared using cross-validation approach for the selection of tuning parameters. Specific practical recommendations for modelling and analyzing Nepa marketing data are provided. Att fördela marknadsföringsbudgeten optimalt är en svår uppgift som alla företag ställs inför. Med uppkomsten av nya marknadsföringstekniker, som reklam på nätet och sociala media, har komplexiteten av data ökat, vilket gör detta problem ännu mer utmanande. Statistiska verktyg för förklarande och prediktiv modellering har vanligtvis använts för att hantera problemet med budgetallokering. Marknadsföringsmix Modellering är en term som omfattar klassen av statistiska metoder som är lämpliga för modellering av den intressanta variabeln (i denna uppsats är det försäljning) när det gäller reklamstrategier och externa variaber, med målet att maximera vinsten genom att konstruera en optimal kombination av marknadsstrategier. Syftet med denna uppsats är att konstruera ett antal modellbyggnadsstrategier, som även inkluderar avancerade regulariseringsmetoder för linjär regression, med en analys av fördelar och nackdelar för varje metod. Flera stora problem som den moderna marknadsföringsmix modellering står inför har beaktats, som till exempel: att välja en passande funktionsformel som bäst beskriver relationen mellan den oberoende variabeln och de beroende variablerna, att hantera marknadsföringens dynamiska omgivningar genom att välja det optimala förfallet hos varje marknadsföringsstrategi, utvärdera säsongsmässiga effekten och marknadsföringsverktygens kollinjäritet. För att överkomma de två vanligaste problemen inom marknadsföringsekonometri, som är multikollinearitet och val av variabler, har regulariseringsmetoder använts. I synnerhet har prestationsnoggrannheten av ridge regression, lasso, naive elastic net och elastic net jämförts- för att ge specifika rekommendationer för Nepa data. Parametrarna för de regulariserade regressionsmetoderna har valts genom korsvalidering. Modellens resultat visar en hög nivå av förutsägelse noggrannhet. Skillnaden mellan nämnda metoder är inte signifikanta för det givna datasetet.

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Avci, Ezgi. "A Comparison Of Some Robust Regression Techniques." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12611165/index.pdf.

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Robust regression is a commonly required approach in industrial studies like data mining, quality control and improvement, and finance areas. Among the robust regression methods Least Median Squares, Least Trimmed Squares, Mregression, MM-method, Least Absolute Deviations, Locally Weighted Scatter Plot Smoothing and Multivariate Adaptive Regression Splines are compared under contaminated normal distributions with each other and Ordinary Least Squares with respect to the multiple outlier detection performance measures. In this comparison a simulation study is performed by changing some of the parameters such as outlier density, outlier locations in the x-axis, sample size and number of independent variables. In the comparison of the methods, multiple outlier detection is carried out with respect to the performance measures detection capability, false alarm rate and improved mean square error and ratio of improved mean square error. As a result of this simulation study, the three most competitive methods are compared on an industrial data set with respect to the coefficient of multiple determination and mean square error.

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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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Submitted by VICTOR COSTA LEDA null (victorleda@gmail.com) on 2016-09-20T13:48:09Z No. of bitstreams: 1 Dissertação Victor Costa Leda Final.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-09-22T19:30:58Z (GMT) No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) Made available in DSpace on 2016-09-22T19:30:58Z (GMT). No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) Previous issue date: 2016-07-28 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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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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Elving, Hannes. "Kvicksilversituationen i Stockholms skärgård." Thesis, Uppsala University, LUVAL, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-113328.

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Kvicksilverutsläppen har på senare år minskat i omfattning men utsläpp sker fortfarande och det finns ett stort lager i naturen. Oorganiskt kvicksilver är relativt ofarligt för levande organismer, men i akvatiska miljöer kan sulfatreducerande bakterier under syrefria förhållanden omvandla oorganiskt kvicksilver till den betydligt mer skadliga formen metylkvicksilver, meHg, som även är mer biotillgängligt än oorganiskt kvicksilver. Kvicksilver biomagnifieras uppåt i trofinivåerna, från att vara lägst halter i plankton och bottenfauna till att vara högst halter i rovfiskar. Kvicksilver ackumuleras även i fiskars muskelvävnad vilket innebär att yngre fiskar har lägre Hg-halter än gamla och stora individer. På grund av detta finns kostråd framtagna av Livsmedelsverket. Saluförd abborre, liksom ett flertal andra fiskarter, får inneha maximalt 0,5 mg Hg/kg fiskmuskel (färskvikt) och för gädda är gränsen satt till 1 mg Hg/kg fiskmuskel (färskvikt). Organiskt kvicksilver kan påverka foster negativt, och därför bör kvinnor i fertil ålder i största möjliga mån undvika kvicksilverrik fisk. Examensarbetet syftar till att belysa kvicksilversituationen i Stockholms skärgård för stationär kustfisk. Information har samlats in dels genom en litteraturstudie och dels genom fältundersökningar utförda av IVL Svenska Miljöinstitutet. Genom statistisk analys och stegvis multipel regression visades att kvicksilverhalten i sediment, vattnets klorofyllhalt och områdets vattenvolym bidrog med förklaringsgrad till halten Hg i fisk. Utifrån de mest signifikanta förklarande variablerna togs en statistisk modell fram i syfte att kunna prediktera teoretiska kvicksilverhalter i fisk. Olika modellkombinationer testades och utgående från en modellvalidering valdes den bästa modellen ut. Dess förklaringsgrad är 81 % där kvicksilverhalten i sediment är modellens enda variabel. Kvicksilversituationen i Stockholms skärgårds kustområden visades grafiskt med hjälp av interpolerade GIS-kartor. Samma områdesavgränsningar användes som i SMHI:s havsområderegister. Det visades att situationen över lag är relativt god, med undantag för de centrala delarna av Stockholm och på så vis kan Stockholm ses som en förorenande punktkälla. Mercury emissions have been reduced in recent years but the discharges still exist and there exist a great mercury stock out in the nature. Inorganic mercury is relatively harmless for living organisms but during shortage of oxygen, sulphur reducing bacteria can transform inorganic mercury into the considerable more toxic substance methyl mercury, meHg, that are more bio available than inorganic mercury. Mercury biomagnify upwards the trophic levels, where the lowest content exists in algae and sediment living fauna and the highest content exist in predator fishes. Mercury also accumulates in fishes muscle tissue, meaning that young fishes have lower mercury content than old and big individuals. Food advices from the Swedish food agency exist because of this. Perch offered for sale are allowed to have a content of maximum 0.5 mg Hg/kg fish muscle (wet weight) and for pike the upper boundary content is 1 mg Hg/kg fish muscle (wet weight). Inorganic mercury can affect foetus negatively, and therefore women in fertile age should avoid fish with high mercury content for as long as possible. The aim of this exam work is to illustrate the mercury situation in the archipelago of Stockholm regarding stationary coast fish. Information was collected through a literature study and by field examines done by IVL Swedish Environmental Research Institute Ltd. Through statistical analyses and stepwise multiple regression it was shown that the mercury content in sediment, chlorophyll and the water volume of the area contributed by statistical explanation to the mercury concentration in fish. On the basis of the most significant variables a statistical model was created, with the purpose of predicting theoretical mercury contents in fish. Different model combinations were tested and by a model validation the best model were chosen. Its statistical explanation is 81 % were the mercury content in the sediment is the only input variable to the model. The mercury situation in the Stockholm archipelago was presented graphically by interpolated maps created with GIS. The same area boundary as SMHI’s maritime registry was used. It was shown that the situation in general is relatively good, with the exception of the central parts of Stockholm. Because of this Stockholm can be seen as a polluting point source.

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Souza, LÃvia dos Santos. "Determinants of product innovation implementation and / or process by Brazilian companies." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13061.

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nÃo hÃ A inovaÃÃo Ã um processo dinÃmico que possibilita a implementaÃÃo de algo novo ou substancialmente aperfeiÃoado, podendo suscitar mudanÃas tanto na empresa quanto no contexto socioeconÃmico o qual estÃ inserido. Os resultados inovativos sÃo diversos, uma vez que sua prÃpria criaÃÃo pode encadear novos processos ou melhorias nos procedimentos e tÃcnicas empregados na produÃÃo ou no fornecimento de serviÃos. A proposta deste trabalho Ã analisar quais os fatores determinantes da implementaÃÃo de inovaÃÃes de produto e/ou processo pelas empresas brasileiras. Para este fim, se utilizou dados provenientes da Pesquisa de InovaÃÃo (PINTEC 2011), realizada pelo Instituto Brasileiro de Geografia e EstatÃstica (IBGE), com apoio do MCTI e FINEP. Os dados tÃm como referÃncia o total Brasil de empresas do setor industrial e de serviÃo, no perÃodo de 2009 a 2011. Com base na literatura, construiu-se um modelo economÃtrico relacionando fatores que podem influenciar o percentual de empresas que implementam inovaÃÃes. Algumas hipÃteses foram levantadas e testadas empiricamente. O modelo tem como variÃveis independentes o apoio do governo, relaÃÃes de cooperaÃÃo com outras organizaÃÃes, dispÃndio em atividades inovativas sobre a receita lÃquida de vendas, pessoas ocupadas em atividades internas de P&D com nÃvel superior e financiamento por recursos de terceiros, mensuradas proporcionalmente ao nÃmero de empresas investigadas. Os resultados obtidos confirmam a hipÃtese formulada de que o apoio do governo estÃ positivamente relacionado ao percentual de empresas brasileiras que implementam inovaÃÃes de produto e/ou processo. E, no caso de inovaÃÃo de processo, dispÃndios em atividades inovativas e qualificaÃÃo profissional tambÃm se mostraram como fatores significativos. A significÃncia global do modelo confirmou a atuaÃÃo conjunta dos fatores como determinantes da implementaÃÃo de inovaÃÃes de produtos e/ou processos novos ou substancialmente aprimorados. Innovation is a dynamic process that enables the implementation of something new or substantially improved, changes may raise both the company and the socioeconomic context which is inserted. Innovative results are various, since his own creation can chain new processes or improvements to procedures and techniques employed in the production or supply of services. The purpose of this project is to analyze the determinants of implementation of innovations in product or process by braziliancompanies. For this purpose, used data from the Research of Innovation ( PINTEC 2011), conducted by the Instituto Brasileiro de Geografia e EstatÃstica (IBGE), with support from FINEP and MCTI. The data has reference to the total Brazil, the companies in the industrial and service sector in the period 2009-2011. Based on the literature, constructed an econometric model relating factors that can influence the percentage of companies that implement innovations. Some hypotheses were developed and tested empirically. The model has as independent variables the government's support, cooperative relationships with other organizations, expenditure on innovation activities of the sales revenue, persons employed in internal R&D with degree and funding from third resources, measured in proportion to the number companies investigated. The results confirm the hypothesis formulated that government support is positively related to the percentage of brazilian companies that implement product innovations and/or process. And in the case of process innovation, expenditure on innovation activities and professional qualification also proved to be significant factors. The global significance of the model confirmed the joint action of the factors as determinants of the implementation of product innovations and/or new or significantly improved processes.

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Júnior, Antônio Carlos Pacagnella. "A inovação tecnológica nas indústrias do Estado de São Paulo: uma análise dos indicadores da PAEP." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/96/96132/tde-25072006-151430/.

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A inovação tecnológica desempenha um papel fundamental no desenvolvimento de empresas, regiões e mesmo de países. Especificamente no estado de São Paulo, estudar os aspectos relevantes a este tema é de suma importância por se tratar do estado mais industrializado e mais importante economicamente no Brasil. Dentro deste contexto, este estudo visa analisar especificamente aspectos ligados à inovação tecnológica nas empresas dos diversos setores de atividade industrial, utilizando para isto ndicadores de inovação tecnológica e de dados empresariais da Pesquisa de Atividade Econômica Paulista (PAEP), realizada pela fundação Sistema Estadual de Análise de Dados Estatísticos (SEADE), sobre o período de 1999 a 2001. The technological innovation performs a fundamental part in the development process of companies, regions and even countries. Specifically in the state of São Paulo, the study of relevant aspects to this theme is of summary importance because it is the most industrialized and economically important in this country. Within of this context, this study aim to analyze specifically some aspects linked to the technological innovation in different sections of industrial activity, using to this, technological innovation indicators and business results obtained by the Paulista Research of Economic Activities (PAEP), that was realized by SEADE foundation over the period of 1999 to 2001.

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Ndiritu, Gachiri Charles. "An Application of Multiple Regression in Exchange Rate Arrangements." Thesis, University of the Western Cape, 2008. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_1863_1263418792.

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This project &quot An application of multiple regression in exchange rate arrangement&quot focused on the processes followed by different countries when choosing an exchange rate regime for currency stabilization. It analyses the consequences faced by emerging markets as a result of changes in volatility of developed countries&rsquo currencies (American Dollar, Japanese Yen, EURO, British Pound and the Canadian Dollar).

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ITAKURA, Fumitada, Kazuya TAKEDA, Katsunobu ITOU, and Weifeng LI. "Single-Channel Multiple Regression for In-Car Speech Enhancement." Institute of Electronics, Information and Communication Engineers, 2006. http://hdl.handle.net/2237/15051.

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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 application Denna 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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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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Marques, Nelson Sousa. "Intelligent system for associative pattern identification in data." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/16840.

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Mestrado em Gestão de Sistemas de Informação Os resultados das ferramentas estatísticas são baseados em resultados numéricos onde a interpretação e compreensão do que está gerado passa pelo intérprete que está a analisar os resultados. Esta tarefa de compreensão é muitas vezes complicada por vários fatores sendo um dos quais o facto do intérprete não conseguir captar dos resultados o que é relevante para avaliar o modelo formulado, não conseguindo avalia-lo como válido ou não, o que poderá levar à utilização de modelos que podem ser descabidos e sem fundamento. Com esta ideia em consideração foi desenvolvido, em ambiente Linux, um pequeno sistema com técnicas de data mining de carácter associativo. Neste sistema é gerado um relatório por cada modelo, onde são analisados os fatores mais relevantes para a criação de modelos, guiando desta forma o intérprete a decidir validar e utilizar o modelo criado ou a rejeitá-lo. O objetivo deste trabalho passou pela aprendizagem da linguagem Python aplicado a dados, uma aprendizagem aprofundada sobre data mining, as técnicas e métodos existentes e uma verificação das ferramentas de machine learning, de modo a criar como produto final um sistema com algumas técnicas. Foi possível a realização do trabalho proposto com a criação do sistema. Foram formulados métodos para produzir um modelo de regressão linear múltipla, regressão logística, um modelo de correlação linear e um modelo de regras de associação. Para três modelos foram gerados métodos tendo por base bibliotecas e machine learning. Para as regras de associação foi criado um método de raiz baseado no algoritmo FP-Growth. For many people statistics is a difficult task to be done, where the output that is given from the analytic tools can be complicated to understand. With this idea it was investigated the possibility of creation of a system that provides the creation some models to the users where is provided some guidelines about the most important values to take care for each model. The goals of this project are the development of the knowledge about Data Mining, learn how to use Python to produce data analysis, verify the existent machine learning applied to data for Python and use some data mining techniques to create a small system for associative models. The system is capable to perform a Linear Regression, a Logistic Regression, a Correlation Coefficient and an Association Rule Mining algorithm. For each method is provided an output that contains the numerical results of the method and it was produce some guidelines with general ideas, assumptions of each method and it is interpreted the most important statistical values to facilitate the understanding of all the methods. The system was developed in Python. Three methods were created are based on machine learning algorithms. The association rule mining algorithm was created from the beginning. The association rule mining algorithm developed was FP-growth. The system was ready to run in Linux. info:eu-repo/semantics/publishedVersion

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Pirie, Iain W. S. "A comparison of some alternatives to least squares multiple regression." Thesis, University of Aberdeen, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318910.

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Multiple linear regression techniques are often employed in the statistical analysis of data. The most frequently used regression procedure is ordinary least squares. However, it is accepted that the method of least squares does not necessarily provide either accurate estimates of unknown regression coefficients or accurate predictions at future data points. Several classes of biased estimators have emerged as possible alternatives to ordinary least squares. We review the origins, definitions and properties of existing biased estimation procedures such as ridge, shrinkage, principal components and partial least squares regression. In addition, two new classes of estimator, multistage and multistep, are introduced. Simulation is the obvious means for assessing the relative merits of different estimation procedures. We review the design and results of previous simulation studies in which comparisons have been made between the performances of different estimation procedures. The designs of most previous studies are somewhat limited and unrealistic. Consequently, few clear guidelines have emerged regarding the circumstances in which individual procedures should either be applied or avoided. To provide some clarification, we conducted a series of simulation experiments that were designed to compare the performances of different regression procedures over a broad range of realistic situations.

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Pereira, Janaina Luiza Lobato. "Permeability prediction from well log data using multiple regression analysis." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3368.

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Thesis (M.S.)--West Virginia University, 2004. Title from document title page. Document formatted into pages; contains xiii, 82 p. : ill. (some col.), maps. Vita. Includes abstract. Includes bibliographical references (p. 41).

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Lansryd, Lisette, and Birr Madeleine Engvall. "GDPR ́s Impact on Sales at Flygresor.se: A Regression Analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254297.

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The possible effects of the General Data Protections Regulations (GDPR) have been widely discussed among policymakers, stakeholders and ordinary people who are the objective for data collection. The purpose of GDPR is to protect people’s integrity and increase transparency for how personal data is used. Up until May 25th, 2018 personal data could be sampled and used without consent from users. Many argue that the introduction of GDPR is good, others are reluctant and argue that GDPR may harm data-driven companies. The report aims to answer how GDPR affects sales at the flight search engine Flygresor.se. By examining how and to what extent these regulations impact revenue, it is hoped for that these findings will lead to a deeper understanding of how these regulations affect businesses. Multiple linear regression analysis was used as the framework to answer the research question. Numerous models were constructed based on data provided by Flygresor.se. The models mostly included categorical variables representing time indicators such as month, weekday, etc. After carefully performing data modifications, variable selections and model evaluation tests three final models were obtained. After performing statistical inference tests and multicollinearity diagnostics on the models it could be concluded that an effect from GDPR could not be statistically proven. However, this does not mean that an actual effect of GDPR did not occur, only that it could not be isolated and proven. Thus, the extent of the effect of GDPR is statistically inconclusive. De möjliga följderna av införandet av General Data Protections Regulations (GDPR) har varit väl omdiskuterat bland beslutsfattare, intressenter och människor som är målet för datainsamlingen. Syftet med GDPR är att skydda människors integritet samt öka insynen för hur personlig data används. Fram tills den 25 maj 2018 har det varit möjligt att samla in och använda personuppgifter utan samtyckte från användare. Många menar att införandet av GDPR är nödvändigt medans andra är mer kritiska och menar att GDPR kan skada lönsamheten för data beroende verksamheter. Denna rapport syftar till att svara på huruvida GDPR har påverkat försäljningen på flygsökmotorn Flygresor.se. Genom att undersöka om och i vilken utsträckning dessa regler påverkat intäkterna, är förhoppningen att dessa resultat kan leda till en djupare förståelse för hur GDPR påverkar företag. Multipel linjär regressionsanalys användes som ramverk för att svara på frågeställningen. Flera modeller utformades baserat på data som tillhandahölls av Flygresor.se. Modellerna var främst baserade på kategoriska variabler som representerade tidsaspekter så som månad, veckodag etc. Efter ett grundligt genomförande av data modifieringar, variabelselektion och modellutvärdering kunde tre modeller konstateras. Efter att ha genomfört signifikanstester och korrelationstester på modellerna kunde det fastställas att en effekt från GDPR inte kunde statistiskt säkerställas. Dock betyder detta inte att GDPR inte har haft en faktisk effekt, utan att en effekt inte kunde isoleras och bevisas.

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Keilani, Mohamed, and Markos Kokkinos. "Familjeföretag : En jämförande studie mellan familjeföretag och icke-familjeföretag med avseende på prestation." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-19512.

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Syftet med denna studie är att ta reda på huruvida det föreligger en skillnad i företagsprestation mellan familjeföretag och icke-familjeföretag. För att göra detta har en kvantitativ metod tillämpats. Undersökningsåren är 2003-2011 och företagen som har undersökts har varit små och medelstora företag på den svenska marknaden. Slutsatsen är att det inte föreligger någon prestationsskillnad mellan familjeföretag och icke-familjeföretag. The purpose with this study is to find out whether there is a difference in firm performance between family firms and non-family firms. In order to fulfill the purpose we have used a quantitative method. The examined period was 2003-2011 and the investigated firms have been small and medium sized firms on the Swedish market. The conclusion drawn is that there is no significant difference in the firm performance between family firms and non-family firms.

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Bastos, Claudio. "MODELOS DE PREVISÃO DE RECURSOS PARA ANTIMICROBIANOS NO HOSPITAL UNIVERSITÁRIO DE SANTA MARIA." Universidade Federal de Santa Maria, 2009. http://repositorio.ufsm.br/handle/1/8122.

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The scarce resources of public health makes the administrator manage the destination of resources, aiming to rationalize and optimize its collection, in order to improve the assistance to patients because the hospital is a public institution and does not get profits but promotes the community well-being. Thus, the hospital infection is acquired after the patient comes to the hospital of after he goes home and might be associated with his staying in hospital or with hospital procedures. This cost must be avoided. Once the complete eradication is not impossible, it is necessary to analyze and to control the monthly cost of the main antibiotics used for its treatment so that there is enough knowledge to foresee the resource collection to buy them. In this context, the main reason of this research is to carry out a forecast of the monthly cost and of the resource collection needed to purchase those medicine used in the treatment of hospital infections at the University Hospital of Santa Maria. To do so, a methodology for forecast by dynamic and multiple linear regressions was used. They were combined with to a multivariate technique by principal components. The technique of principal components was used to eliminate the multiple linearity existing among the original variants so, the resulting principal components were used as variables in the construction of the model of multiple linear regression and of dynamic regression. Therefore, these methodologies are applied to a case study of public health, in order to foresee and to conclude about which model is more suitable to forecast the monthly cost of antibiotics in hospital infections. The results obtained from the two models were considered satisfactory but the model of dynamic regression was chosen to be more suitable because it presented a mean absolute percentage error (MAPE). Finally, the findings might be a managerial tool for hospital administration when they offer subsides for the budget of planning and of the resource finances, especially in a time when resources are globally scarce, making health even more expensive. Os escassos recursos da saúde pública impõem ao administrador gerenciar a destinação dos recursos buscando racionalizar e otimizar sua alocação, permitindo, desta forma, melhorar o atendimento aos pacientes, pois o hospital, sendo uma entidade pública, não tem por objetivo o lucro, mas sim promover o bem estar da comunidade. Com isso, a infecção hospitalar que é adquirida após a internação do paciente e se manifesta durante a internação ou mesmo após a alta, podendo ser relacionada com a internação ou procedimentos hospitalares, deve ser evitada. Uma vez que sua total erradicação não é possível, se faz necessário analisar e controlar o custo mensal dos principais antibióticos utilizados no seu tratamento a fim de se ter embasamento suficiente para prever a alocação de recursos para sua aquisição. Nesse contexto, o principal objetivo desta pesquisa é realizar a previsão do custo mensal e de alocação de recursos necessários para aquisição de medicamentos utilizados no tratamento de infecções hospitalares no Hospital Universitário de Santa Maria. Para isso, utilizou-se a metodologia de previsão por regressão linear múltipla e de regressão dinâmica combinada com a técnica multivariada de componentes principais que foi utilizada para eliminar a multicolinearidade existente entre as variáveis originais. Com isso, as componentes principais resultantes foram utilizadas como variáveis independentes na construção do modelo de regressão linear múltipla e de regressão dinâmica. Portanto, essas metodologias são aplicadas a um estudo de caso na saúde pública, a fim de fazer previsões e tirar conclusões a respeito de qual modelo é mais adequado para realizar a previsão do custo mensal dos antibióticos em infecções hospitalares. Os resultados obtidos nos dois modelos foram considerados satisfatórios, mas foi escolhido, como modelo mais adequado para realizar as previsões, o modelo de regressão dinâmica, porque apresentou o menor erro percentual absoluto médio (MAPE). Por fim, as previsões encontradas, podem se constituir em uma ferramenta gerencial para a administração hospitalar ao fornecer subsídios para o planejamento orçamentário e financeiro dos recursos, especialmente em uma época em que há escassez de recursos em escala global, com reflexos muito intensos nos custos da saúde.

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Adusei, Emmanuel. "The use of wavelength-dispersive x-ray fluorescence (WDXRF) spectroscopy and multivariate techniques for the assessment of illegal dyes in spices." Master's thesis, Instituto Superior de Ciências da Saúde Egas Moniz, 2014. http://hdl.handle.net/10400.26/6713.

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Dissertação de mestrado Erasmus Mundus para obtenção do grau de mestre em Técnicas Laboratoriais Forenses Sudan dyes are synthetic azo and diazo compounds that are banned for use in food worldwide including the European Community due to their potential toxicity to humans. The ability of WDXRF spectroscopic technique to predict the levels of adulteration of paprika and sweet pepper suspected to be adulterated with Sudan I-IV, Para Red and Sunset Yellow FCF dyes was investigated in this study. Logistic regression and discriminant analysis classification models were developed to predict the type of adulteration using WDXRF spectral features such as the Compton and Rayleigh scatter intensities and the Compton and Rayleigh ratios. Prediction of the levels of adulteration was assessed by using multiple regression analysis. 83% of the 210 adulterated samples were correctly classified by the logistic regression with 90% sensitivity, 75%specificity with a prediction power of 92% into respective adulteration groups. 86% and 90% correct prediction were obtained for discriminant analysis models with 94% sensitivity and 74% specificity. Three multiple regression models were performed for each data set. Models based on the Compton and Rayleigh ratios, Compton and Rayleigh scatter intensities as well as a full model based on both ratios and scatter intensities were developed and compared with each other. The full model revealed to be the best model to predict the levels of adulteration with an adjusted R2 between 95.1 to 99.0% with the errors of estimate between 1.6 to 3.7%. To the best of the author’s knowledge, this is the first time such methodological approach was used for the purposes of screening illegal dyes in food matrices. Moreover, WDXRF technique may represent a promising tool for the screening of Sudan dyes-adulterated spices and can be used as an alternative to classical methods for determining Sudan dyes present in food.

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Guo, Xu. "Checking the adequacy of regression models with complex data structure." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/90.

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In this thesis, we investigate the model checking problem for parametric regression model with missing response at random and nonignorable missing response. Besides, we also propose a hypothesis-adaptive procedure which is based on the dimension reduction theory. Finally, to extend our methods to missing response situation, we consider the dimension reduction problem with missing response at random. The .rst part of the thesis introduces the model checking for parametric models with response missing at random which is a more general missing mechanism than missing completely at random. Di.erent from existing approaches, two tests have normal distributions as the limiting null distributions no matter whether the inverse probability weight is estimated parametrically or nonparametrically. Thus, p-values can be easily determined. This observation shows that slow convergence rate of nonparametric estimation does not have signi.cant e.ect on the asymptotic behaviours of the tests although it may have impact in .nite sample scenarios. The tests can detect the alternatives distinct from the null hypothesis at a nonparametric rate which is an optimal rate for locally smoothing-based methods in this area. Simulation study is carried out to examine the performance of the tests. The tests are also applied to analyze a data set on monozygotic twins for illustration. In the second part of the thesis, we consider model checking for general linear regression model with non-ignorable missing response. Based on an exponential tilting model, we .rst propose three estimators for the unknown parameter in the general linear regression model. Three empirical process-based tests are constructed. We discuss the asymptotic properties of the proposed tests under null and local alternative hypothesis with di.erent scenarios. We .nd that these three tests perform the same in the asymptotic sense. Simulation studies are also carried out to assess the performance of our proposed test procedures. In the third part, we revisit traditional local smoothing model checking procedures. Noticing that the general nonparametric regression model can be considered as a special multi-index model, we propose an adaptive testing procedure based on the dimension reduction theory. To our surprise, our method can detect local alternative at faster rate than the traditional optimal rate. The theory indicates that in model checking problem, dimensionality may not have strong impact. Simulations are carried out to examine the performance of our methodology. A real data analysis is conducted for illustration. In the last part, we study the dimension reduction problem with missing response at random. Based on the work in this part, we can extend the adaptive testing procedure introduced in the third part to the missing response situation. When there are many predictors, how to e.ciently impute responses missing at random is an important problem to deal with for regression analysis because this missing mechanism, unlike missing completely at random, is highly related to high-dimensional predictor vector. In su.cient dimension reduction framework, the fusion-re.nement (FR) method in the literature is a promising approach. To make estimation more accurate and e.cient, two methods are suggested in this paper. Among them, one method uses the observed data to help on missing data generation, and the other one is an ad hoc approach that mainly reduces the dimension in the nonparametric smoothing in data generation. A data-adaptive synthesization of these two methods is also developed. Simulations are conducted to examine their performance and a HIV clinical trial dataset is analysed for illustration. Keywords: Model checking; Inverse probability weight; Non-ignorable missing response; Adaptive; Central subspace; Dimension reduction; Data-adaptive Synthesization; Missing recovery; Missing response at random; Multiple imputation.

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Lee, Jun Ho, and Noel Mattar. "Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254280.

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There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject.There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject. Det finns en märkbar trend av växande turism världen över. Turismen in i Sverige får allt mer ekonomisk och social uppmärksamhet. Syftet med detta arbete är att finna vilka makroekonomiska variabler som bidrar till de årliga intäkterna av internationell turism i Sverige. För analysen används multipel linjär regression över tidsperioden 1978-2017. Det slutgiltiga resultatet visar att BNP är den dominanta makroekonomiska faktorn som är drivande i de årliga intäkterna av internationell turism i Sverige, detta oavsett tidsperiod. Valutakursen NOK/SEK verkar vara signifikant i det långa loppet, från 1978-2017, men inte under de kortare tidsperioderna. Valutakursen USD/SEK och arbetslösheten är båda icke signifikanta variabler för internationell turism konsumtion i Sverige över alla tidsperioder. Devalveringen av den svenska kronan år 1992, förändrade inte relationen mellan de sistnämnda variablerna och y -variabeln. Dock kan dessa resultat vara ostabila på grund av de begränsade antalet observationer som använts i analysen och därför rekommenderar vi andra regressions modeller till detta ämne, såsom "panel data regression".

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Sundström, Stina. "Coding in Multiple Regression Analysis: A Review of Popular Coding Techniques." Thesis, Uppsala University, Department of Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126614.

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Simmons, Carol Ivy. "A Logistic Regression Analysis of Multiple Independent Variables Impacting Psychiatric Readmissions." Thesis, Capella University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10289773.

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This dissertation explored several internal and external factors in relation to psychiatric readmissions. Internal factors are directly related to the individual i.e., demographic information, diagnosis, admission history and status. External factors are factors outside of the individuals control i.e., length of hospital stay and reimbursement processes. The goal of the study was to explore the impact of multiple factors in relation to the phenomenon of psychiatric readmissions. Dynamic Systems Theory (1994) was used as a theoretical foundation to understand the complexities associated with psychiatric readmissions. The study utilized state archival data provided by the Maryland Health Services Cost Review Commission; an agency charged with collecting statewide hospital data on hospital admissions. A quasi experimental study was conducted using a logistic regression design to answer the research question: When taken together do age, sex, ethnicity, diagnosis, insurance type, admission status and length of stay predict psychiatric readmission? This researcher predicted that the null hypothesis will be rejected. The sample included a large state-wide data set of over 130,000 individuals who fell under the criteria of being over the age of 18 when readmitted for psychiatric care in Maryland in 2015. The research methodology includes a logistic regression research design, exploring multiple factors, simultaneously, that impact psychiatric readmissions. The results of the study indicate that length of stay is the most important factor impacting psychiatric readmissions. The second most important factor associated with psychiatric readmission, is a psychiatric readmission within 30 days. Medicare and Medicaid were also found to be significant factors associated with psychiatric readmission. Additionally, affective disorders were found to be the primary diagnosis associated with psychiatric readmissions. Lastly, individuals at greatest risk for psychiatric readmissions are between the age of 18-39, are non-Hispanic, are enrolled in Medicare, most likely to be disabled, are diagnosed with an affective disorder and have had a previous psychiatric readmission.

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Gaffert, Philipp Verfasser], and Susanne [Akademischer Betreuer] [Rässler. "Multiple Imputation via Local Regression (Miles) / Philipp Gaffert ; Betreuer: Susanne Rässler." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://d-nb.info/1147756910/34.

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Tailoka, Frank Patson. "An efficient strategy for the selection of acceptable multiple regression models." Thesis, University of Essex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265195.

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Cui, Chenhao. "Nonlinear multiple regression methods for spectroscopic analysis : application to NIR calibration." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10058694/.

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Chemometrics has been applied to analyse near-infrared (NIR) spectra for decades. Linear regression methods such as partial least squares (PLS) regression and principal component regression (PCR) are simple and widely used solutions for spectroscopic calibration. My dissertation connects spectroscopic calibration with nonlinear machine learning techniques. It explores the feasibility of applying nonlinear methods for NIR calibration. Investigated nonlinear regression methods include least squares support vec- tor machine (LS-SVM), Gaussian process regression (GPR), Bayesian hierarchical mixture of linear regressions (HMLR) and convolutional neural networks (CNN). Our study focuses on the discussion of various design choices, interpretation of nonlinear models and providing novel recommendations and insights for the con- struction nonlinear regression models for NIR data. Performances of investigated nonlinear methods were benchmarked against traditional methods on multiple real-world NIR datasets. The datasets have differ- ent sizes (varying from 400 samples to 7000 samples) and are from various sources. Hypothesis tests on separate, independent test sets indicated that nonlinear methods give significant improvements in most practical NIR calibrations.

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Gaffert, Philipp [Verfasser], and Susanne [Akademischer Betreuer] Rässler. "Multiple Imputation via Local Regression (Miles) / Philipp Gaffert ; Betreuer: Susanne Rässler." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://nbn-resolving.de/urn:nbn:de:bvb:473-opus4-498847.

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Hall, Kimberlee K., and Phillip R. Scheuerman. "Development of Multiple Regression Models to Predict Sources of Fecal Pollution." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/2880.

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This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant (p < 0.001) but with low r2 values (Model 1 r2 = 0.02, Model 2 r2 = 0.01, Model 3 r2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.

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Garriga, Nogales Edgar 1990. "New algorithmic contributions for large scale multiple sequence alignments of protein sequences." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2022. http://hdl.handle.net/10803/673526.

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In these days of significant changes and the rapid evolution of technology, the amount of datascience has to deal with the growth incredibly fast, and the size of data could be prohibitive.Multiple Sequence Alignments (MSA) are used in various areas of biology, and the increase ofdata has produced a degradation of the methods. That is why is proposed a new solution toperform the MSA. This novel paradigm allows the alignment of millions of sequences and theability to modularize the process. Regressive enables the parallelization of the process and thecombination of clustering methods (guide-tree) with whatever aligner is desired. On theclustering side, the guide-tree has to be rethought. A study of the current state of the methodsand their strength and weaknesses have been performed to shed some light on the topic. Theguide-tree cannot be the bottleneck, and it should provide a good starting point for the aligners. En aquests dies de profunds canvis i una ràpida evolució de la tecnologia, la quantitat de dataque la ciència ha de treballar ha crescut increïblement ràpid i la grandària dels arxius ha crescutde manera quasi prohibitiva.Els alineaments múltiples de seqüència (MSA) es fan servir endiverses àrees de la biologia, i l'increment de les dades ha produït una degradació delsresultats. És per això, que es proposa una nova estratègia per realitzar els alineaments. Aquestnou paradigma permet alinear milions de seqüències i l'opcio de modularitzar el procés.'Regressive' permet la paral·lelització del procés i la combinació de diferents algoritmesd'agrupacio (guide-tree) amb el mètode de alineament que és desitgi. Dins del camp del'agrupació, s'ha de repensar l'estratègia per crear els guide-tree. Un estudi sobre l'estat actualdels mètodes i les seves virtuts i punts febles ha sigut realitzar per llençar una mica de llum enaquesta àrea. Els 'guide-tree' no poden ser el coll de botella, i haurien de servir per començarde la millor manera possible el procés d'alineament.

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Pan, Wensi. "Comparison of Imputation Methods on Estimating Regression Equation in MNAR Mechanism." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175772.

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In this article, we propose an overview of missing data problem, introduce three missing data mechanisms and study general solutions to them when estimating a linear regression equation. When we have partly missing data, there are two common ways to solve this problem. One way is to ignore those records with missing values. Another method is to impute those observations being missed. Imputation methods arepreferred since they provide full datasets. We observed that there is not a general imputation solution in missing not at random (MNAR) mechanism. In order to check the performance of existing imputation methods in a regression model, a simulation study is set up. Listwise deletion, simple imputation and multiple imputation are selected into comparison which focuses on the effect on parameter estimates and standard errors. The simulation results illustrate that the listwise deletion provides reliable parameter estimates. Simple imputation performs better than multiple imputation in a model with a high determination coefficient. Multiple imputation,which offers a suitable solution for missing at random (MAR), is not valid for MNAR.

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Seega, Niklas Christoph. "Determinanten von Revisionskosten börsenkotierter Industrie- und Dienstleistungsunternehmen in der Schweiz." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/00645697002/$FILE/00645697002.pdf.

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Kippert, Carlos Junior. "Avaliação do desempenho na pré e pós-desmama para diferentes grupos genéticos em uma população multirracial Aberdeen Angus-Nelore." Universidade Federal de Santa Maria, 2006. http://repositorio.ufsm.br/handle/1/10893.

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The objective of this study was to evaluate the breed additive genetic and heterozygotic effects, direct and maternal, on the weight and the average daily gain for a multibreed population Aberdeen Angus-Nellore. They were studied the characteristics adjusted weight to 205 days of age (P205) and to 550 days of age (P550) and average daily gain from birth to weaning (GMD_ND) and from weaning to 550 days of age (GMD_DS). The data was furnished by Gensys Consultores Associados S/C Ltda. and Natura Genética Sul Americana, and it contained records on 121,241 animals sired by 1,359 bulls and 84,465 cows, raised in 104 farms in differents brazilians states from 1986 to 2002. The data was analyzed by two different methodologies: Genetic Group Model (MGG) and Multiple Regression Method (MRM). In paper number one, pre-weaning period, it were used records on 96,502 animals, sired by 962 bulls and 67,231 cows, grouped in 2,542 contemporaneous groups (GC) and 45 genetic groups (GG). The model used for the analysis by MGG, included as fixed, the effects of contemporaneous group-GC (including the animals born at the same farm, year and season and belonging to the same sex and management group), genetic group-GG (genetic additive effect of the animal individual breed, paternal and maternal), and the covariables age of the animal at weaning (IDD) and age of the cow at parturition (IVP), as linear and quadratic effects. For the MRM analysis, the data was firstly adjusted for the effects of GC, and for the covariables IDD and IVP, linear and quadratic. After that, it was performed a MRM considering the direct and maternal additive genetic effect of the breed and the direct and maternal heterozigosity. In the paper number two, post-weaning period, it were evaluated performances of 40,310 animals, sired by 720 bulls and 31,136 cows, distributed in 650 contemporaneous groups and 33 genetic groups. The model adopted by MGG considered as fixed, the effects of contemporaneous group (including the animals born at the same farm, year and season and belonging to the same sex and management group at 550 days of age) and the covariables IVP, linear and quadratic effects and the age of the animal to the age standard of 550 days was done (ISA), as linear effect. The MRM analysis was performed at the same way that for paper one. For the pre-weaning period, the results of this work alloud us to conclude that the direct additive genetic effects from Aberdeen Angus breed in relation to Nellore breed give better performances; the direct and maternal heterozygosity effect were high and significant, suggesting that crossed calves and cows can perform better than the pure breeds. For the pre-weaning period the estimatives obtained by MRM were similar to those obtained by MGG. In relation to the post-weaning period it were observed differences in the performances of the animals, showing a better efficiency of Aberdeen Angus breed additive genes in relation to the Nellore breed. By the regression coefficient for direct and maternal effects it is possible to conclude that exist a bigger influence of the direct in relation to the maternal effect, suggesting that, in this phase, the animal performance depends most of its breed composition than of the influence of it mother. In spite of to exist similarities among the estimatives obtained by MGG and MRM, it is not recommended the use of MRM because of the inconsistencies observed in the results by this method. O presente estudo teve por objetivo avaliar os efeitos genéticos aditivos raciais e heterozigóticos, diretos e maternos, sobre o desempenho ponderal de uma população multirracial Aberdeen Angus-Nelore. Foram estudadas as variáveis peso ajustado aos 205 dias de idade (P205), ganho médio diário do nascimento a desmama (GMD_ND), peso ajustado aos 550 dias de idade (P550) e ganho médio diário da desmama ao sobreano. O arquivo de dados foi fornecido pelas empresas Gensys Consultores Associados S/C Ltda. e Natura Genética Sul Americana, e continha informações de desempenho de 121.241 animais filhos de 1.359 touros e 84.465 vacas, criados em 104 fazendas, em diferentes estados brasileiros, coletadas no período entre 1986 e 2002. Os dados foram analisados através de dois modelos: Modelo de Grupo Genético (MGG) e Regressão Múltipla (MRM). No artigo 1, que abrangeu a fase prédesmama, foram utilizadas observações de 96.502 animais, filhos de 962 touros e de 67.231 vacas, agrupados em 2.542 grupos de contemporâneos e 45 grupos genéticos diferentes. O modelo utilizado para a análise através do MGG continha os efeitos fixos de grupo de contemporâneos (fazenda, ano de nascimento, sexo, estação de nascimento e grupo de manejo até a desmama) e grupo genético (efeito aditivo de raça individual, paterno e materno), além das covariáveis idade do bezerro a desmama e idade da vaca ao parto, incluídas como efeito linear e quadrático. Para o modelo de regressão múltipla, os dados foram inicialmente ajustados para o efeito GC, e das covariáveis idade do bezerro a desmama e idade da vaca ao parto, incluídas como efeito linear e quadrático, e então, foi conduzida a análise de regressão múltipla num modelo que considerou o efeito aditivo direto de raça, efeito aditivo materno de raça, heterozigose direta e heterozigose materna. No artigo 2, foi estudada a fase de pós-desmama, avaliando o desempenho de 40.310 animais, filhos de 720 touros e 31.136 vacas, distribuídos em 650 grupos de contemporâneos e 33 diferentes grupos genéticos, onde o modelo utilizado para o MGG continha os seguintes efeitos fixos de grupo de contemporâneos (fazenda, ano e estação de nascimento, sexo e grupo de manejo ao sobreano) e grupo genético, além das covariáveis a idade da vaca ao parto (efeito linear e quadrático) e idade do bezerro ao sobreano (efeito linear). A análise de regressão múltipla foi conduzida de forma similar ao artigo 1. Para a fase pré-desmama conclui-se que os efeitos aditivos diretos da raça Angus em relação à raça Nelore determinaram melhores desempenhos; os efeitos de heterozigose direta e materna foram altos e significativos, indicando um maior desempenho de vacas e bezerros cruzados. Para esta fase as estimativas geradas através de RM foram semelhantes àquelas encontradas através do MGG. Em relação a fase pós-desmama foi possível evidenciar que as diferenças observadas no desempenho dos animais, evidenciam a maior eficiência dos genes de efeito aditivo da raça Angus em contraste com os da raça Nelore e, através dos coeficientes de regressão para os efeitos diretos e maternos, é possível inferir que existe grande influência do efeito direto em relação ao materno, sugerindo que, nesta fase, o desempenho dos animais depende mais da sua própria composição genética racial do que da composição da mãe. Ainda que existam semelhanças entre os métodos de RM e MGG, para esta fase, não se aconselha a utilização da RM, visto da inconstância dos resultados obtidos através deste método.

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Zhang, Anda. "Analys av bostadsrättspriset i Stockholms innerstad : En multipel linjär regression." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167646.

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In this study a multiple linear regression was carried out in the interest of analysing a number of variables effect on the final prices of apartments in Stockholm’s inner districts. The result may be employed to predict and observe percentage changes on the final price of apartments in Stockholm in the future. Five models were constructed after which they were analysed and compared. The construction of these models were supported by data from the real estate agency Erik Olsson. The result of this study displays that living space have the highest positive influence on the final prices. Among all the inner city districts, Östermalm is the district that contributes the most to the final price growth. All five models had a coefficient of determination between 89%-94%. I denna studie används en multipel linjär regression för att analysera ett antal variablers inverkan på bostadsrättspriset i Stockholms innerstad. Resultatet kan användas till att prediktera och betrakta procentuella förändringar för slutpriset av en bostadsrätt i Stockholms innerstad. Fem olika modeller konstruerades varefter de analyserades och jämfördes. Modellerna konstrueras med hjälp av data för alla sålda bostadsrätter i Stockholms innerstad mellan åren 2010 - 2014 från fastighetsförmedlingen Erik Olsson. Resultatet av studien visar bland annat att boarean har störst positiv påverkan på slutpriset. Bland stadsdelarna i innerstaden är Östermalm stadsdelen som bidrar mest till ett dyrare bostadsrättspris. Samtliga modeller hade förklaringsgrader på 89%-94%.

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Fu, Shing. "Regression approaches to estimation of relative risk : application to multiple sclerosis studies." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/29503.

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Using a log link for binary response in generalized linear mixed-effects models (GLMM) allows direct estimation of the relative risk. If a random intercept is the only random effect in the conditional mean structure, the marginal mean has the same form. The fixed effects, representing the log relative risks, have the same interpretation in both the mixed-effects model and the marginal model. This leads to two approaches to estimate the relative risks, 1) maximum likelihood for the mixed-effects models and 2) the generalized estimating equations (GEE) approach for the marginal models. In our study, we apply such log-linear models to assess the effects of neutralizing antibodies on interferon beta-1b in relapsing-remitting multiple sclerosis. The results obtained by the two approaches are compared. The relative efficiency of the GEE approach and the robustness of the GLMM approach to some forms of misspecification of the model for the random effects are studied by simulations.

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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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Gao, Jie. "Lake stage fluctuation study in West-Central Florida using multiple regression models." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000502.

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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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Yin, Ping. "Estimating R2 Shrinkage in Multiple Regression: A Comparison of Different Analytical Methods." DigitalCommons@USU, 1999. https://digitalcommons.usu.edu/etd/6147.

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This study investigated the effectiveness of various analytical methods used for estimating R2 shrinkage in multiple regression analysis. Two categories of analytical formulae were identified: estimators of the population squared multiple correlation coefficient (ρ2), and estimators of the population cross-validity coefficient (ρc2). To avoid possible confounding factors that might be associated with a real data set such as data nonnormality, lack of precise population parameters, different degrees of multicollinearity among the predictor variables, and so forth, the Monte Carlo method was used to simulate multivariate normal sample data, with prespecified population parameters such as the squared multiple correlation coefficient (ρ2), number of predictors, different sample sizes, known degree of multicollinearity, and controlled data normality conditions. Five hundred replicates were simulated within each cell of the sampling conditions. Various analytical formulae were applied to the simulated data in each sampling condition, and the "adjusted" coefficients were obtained and then compared to their corresponding population parameters (ρ2 and ρc2). Analysis of the results indicates that the currently most widely used (in both SAS and SPSS) "Wherry" formula is probably not the most effective analytical formula in estimating ρ2. Instead, the Pratt formula appeared to outperform other analytical formulae across most of these sampling conditions. Among the analytical formulae designed to estimate ρc2, the Browne formula appeared to be the most effective and stable in minimizing statistical bias across different sampling conditions. The study also concludes that it is the n/p (sample size/number of predictor variables) ratio that affects the performances of these analytical formulae the most; different degrees of multicollinearity among predictor variables do not have dramatic influence on the performances of these analytical formulae. Further replicants on both real and simulated data re still needed to investigate the effectiveness of these analytical formulae.

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Wesley, Gordon Brian. "Multiple Regression Analysis of Factors Concerning Cardiovascular Profitability Under Health Care Reform." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1468.

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Cardiovascular (CV) patients receive one-third of the care and account for $444 billion of the health care costs in the United States. The cardiovascular service line (CVSL) in hospitals contributes to the profitability influenced by elements of resource dependence theory (RDT). The purpose of this study was to understand whether the regression model of hospital characteristics and outcomes would predict profitability in a CVSL through the cost-to-charge ratio (CCR). The use of a general linear model and multiple regression analysis to examine the 2012 National Inpatient Sample from the Healthcare Cost and Utilization Project allowed estimates from a weighted sample of discharges from all hospitals in participating states. Transformation to dichotomous, independent variables preceded analysis of CV-conditions by discharges. An analysis of variance included in the validated model of grouped strata predicted a level of profitability through the CCR, (4, 509) = 129.83, p < .001, R2 = .505. Mortality was not a significant predictor in the regression model. The 3 characteristic variables with an inverse relationship to the CCR, which resulted in favorable profitability for CVSL, included large, academic, and private for-profit institutions. Prior research aligns well to the study, which emphasized the importance of RDT. Leaders in health care organizations may choose to employ decision making that is dependent upon big data and reference to internal resources to achieve reform expectations. Predictive modeling may aid in the strategic direction of health care organizations. Social implications of this study include hospitals striving to enhance the value proposition by centering care activities around the person over rationing finite resources by condition.

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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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Lundström, Josefine, and Juoni Ruotsalainen. "Aktivering av trafiksäkerhetskameror : En studie av kameraaktiveringens effekter på fordonshastigheter i Sverige." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12110.

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During 2006 an estimated number of 150 persons are supposed to have been killed in road accidents caused by speed limit violations. Through Automatic traffic security control (ATK) the Swedish road administration (Vägverket) is working towards lowering the number of speed related accidents. By placing the speed cameras on roads they've managed to lower the average speed at those places. The enlargement of the number of speed cameras is based upon knowledge about for example how high the risk is for speed related accidents on the roads. The speed cameras always measure the speed in which every vehicle passes, but aren't constantly activated to register speed violations. Our purpose with this essay is consequently to explore possible relations between the activation of the speed cameras and the speed itself on the roads.We studied the average speed and the number of speed violations during 12 weeks evenly distributed in 2007. To see if the results would differ, we used two different response variables in the analysis. Multiple linear regression was used to analyse the average speed, while Poisson regression was used in the analysis of the number of speed violations. An activated camera proved to cause a lowered average speed and fewer speed violations in three regions (Skåne, Mälardalen, Norr).To study the effect of maximized camera activation, an experiment in the region of Mälardalen was performed in the beginning of 2008. The result showed that maximized camera activation didn't decelerate the average speed, in stead the region's own activation policy seems to be more important for a lowered average speed. When the traffic flow rises the average speed decelerates while the number of speed violations also rises. During the study of commuter traffic we could see that the average speed is lower and there is fewer speed violations on commuter roads compared to normal traffic roads. Under 2006 beräknas 150 personer ha omkommit i vägtrafikolyckor på grund av överskridna hastighetsgränser. Vägverket arbetar för att sänka dessa siffror bland annat genom att använda sig av Automatisk trafiksäkerhetskontroll (ATK). Genom att placera trafiksäkerhetskameror på sträckor har medelhastigheten på dessa sänkts. Trafiksäkerhetskamerorna mäter alltid hastigheten hos varje passerande fordon, men är inte konstant aktiverade för att registrera hastighetsöverträdelser. Nu vill man optimera kameraaktiveringen för att minska antalet ärenden utan att hanteringskapaciteten överskrids. Vårt syfte med uppsatsen är därför att undersöka möjliga samband mellan aktivering av trafiksäkerhetskameror och själva hastigheten på vägarna.Medelhastigheten och antalet överträdelser studerades under tolv veckor jämnt fördelade över år 2007. Analyserna gjordes med två olika responsvariabler för att se om resultaten skilde sig åt. Vi använde oss av multipel linjär regression för att analysera medelhastigheten, medan Poissonregression användes för antalet överträdelser. Det visade sig att en aktiv kamera gav upphov till sänkta medelhastigheter och färre hastighetsöverträdelser i tre regioner (Skåne, Mälardalen, Norr).För att studera effekten av maximal kameraaktivering utfördes ett experiment i region Mälardalen under början av 2008. Det visade sig att en maximal aktivering inte gav en sänkning av genomsnittshastigheterna, istället verkar regionens egen aktiverings-strategi ha större betydelse för sänkta genomsnittshastigheter.När fordonsflödet på alla sträckor ökar så minskar medelhastigheten medan antalet överträdelser ökar. För pendeltrafiksträckor är medelhastigheten lägre och det sker färre hastighetsöverträdelser än på normaltrafiksträckor.

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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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Dissertations / Theses on the topic 'Multiply regression'

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