This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.

Missing data continues to be an issue not only the field of statistics but in any field, that deals with data. This is due to the fact that almost all the widely accepted and standard statistical software and methods assume complete data for all the variables included in the analysis. As a result, in most studies, statistical power is weakened and parameter estimates are biased, leading to weak conclusions and generalizations. Many studies have established that multiple imputation methods are effective ways of handling missing data. This paper examines three different imputation methods (predictive mean matching, Bayesian linear regression and linear regression, non Bayesian) in the MICE package in the statistical software, R, to ascertain which of the three imputation methods imputes data that yields parameter estimates closest to the parameter estimates of a complete data given different percentages of missingness. In comparing the parameter estimates of the complete data and the imputed data, the parameter estimates in each model were evaluated and compared. The paper extends the analysis by generating a pseudo data of the original data to establish how the imputation methods perform under varying conditions.

Resumo: Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção. / Abstract: In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production. / Orientador: Francisco Villarreal Alvarado / Coorientador: Antonio Padilha Feltrin / Banca: Evaristo Bianchini Sobrinho / Banca: José Carlos de Melo Vieira Júnior / Mestre

Made available in DSpace on 2014-06-11T19:22:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-03-22Bitstream added on 2014-06-13T18:08:34Z : No. of bitstreams: 1 silva_mv_me_ilha.pdf: 743779 bytes, checksum: 5aad49dd95d63ada483f753bee811fd7 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, otimiza-se o funcionamento de uma fábrica desidratadora de forragens localizada na Espanha. Esta possui processos seqüenciados, secagem, produção de fardos de feno e produção de grãos, que para serem realizados consomem quantidades distintas de energia. Estabelecem-se então, os períodos de produção para cada processo, juntamente com a quantidade em toneladas a serem produzidas, sabendo que na Espanha a energia elétrica possui vinte e quatro preços, um para cada hora do dia. É proposto um modelo para a função objetivo, utilizando dados históricos de produção (Ton), consumo (kWh) e tempo (h), que retratará o funcionamento da empresa. Este modelo é obtido por meio de regressão linear múltipla e é implementado utilizando o software Lingo. Os resultados dessa implementação fornecerão as horas totais diárias que cada processo deverá ser realizado, juntamente com a quantidade de toneladas de pacotes de feno e grãos, e o custo diário da energia elétrica para realizar a produção. / In this work, optimize of the functioning of a plant that dehydrates fodder plants located in Spain. This possess sequenced processes, drying, production of hay packs and production of grains, which to be carried through consumes distinct amounts of energy. Then, the periods of production for each process are established, together with the amount in tons to be produced, knowing that in Spain the electric energy possess twenty and four prices, one for each hour of the day. It is considered a model for the objective function, by using given historical data of production (Ton), consumption (kWh) and time (h), that the functioning of the company will portray. This model is gotten by means of multiple linear regression and is implemented using software Lingo. The results of this implementation will supply the daily total hours that each process will have to be carried through, with the amount of tons of packages of hay and grains , and the daily cost of the electric energy to carry through the production.

Doctor of Philosophy / Department of Statistics / Weixing Song / Kernel based non-parametric regression is a popular statistical tool to identify the relationship between response and predictor variables when standard parametric regression models are not appropriate. The efficacy of kernel based methods depend both on the kernel choice and the smoothing parameter. With insufficient smoothing, the resulting regression estimate is too rough and with excessive smoothing, important features of the underlying relationship is lost. While the choice of the kernel has been shown to have less of an effect on the quality of regression estimate, it is important to choose kernels to best match the support set of the underlying predictor variables. In the past few decades, there have been multiple efforts to quantify the properties of asymmetric kernel density and regression estimators. Unlike classic symmetric kernel based estimators, asymmetric kernels do not suffer from boundary problems. For example, Beta kernel estimates are especially suitable for investigating the distribution structure of predictor variables with compact support. In this dissertation, two types of Beta kernel based non parametric regression estimators are proposed and analyzed. First, a Nadaraya-Watson type Beta kernel estimator is introduced within the regression setup followed by a local linear regression estimator based on Beta kernels. For both these regression estimators, a comprehensive analysis of its large sample properties is presented. Specifically, for the first time, the asymptotic normality and the uniform almost sure convergence results for the new estimators are established. Additionally, general guidelines for bandwidth selection is provided. The finite sample performance of the proposed estimator is evaluated via both a simulation study and a real data application. The results presented and validated in this dissertation help advance the understanding and use of Beta kernel based methods in other non-parametric regression applications.

The characteristics of vehicle steering perception are decisive factors concerning vehicle safety and overall pleasure behind the wheel. It is a challenge for vehicle manufacturers to achieve these features and qualities, because usually vehicle tuning almost only relies on subjective evaluation of test drivers, which is costly and time consuming. In order to optimize suspension design and develop a tool that can be used to evaluate steering with objective metrics instead of subjective assessment, links between them must be confirmed. In this master thesis, both objective and subjective testing data of over 20 vehicles across four different segments are introduced in linear and nonlinear analysis. Linear regression analysis is applied to investigate simply positive or negative correlation between a pair of subjective-objective parameters. However, even if certain linear correlations are obtained, it is still hard to define the optimal value for objective metrics. Considering that the general shape of a correlation function can reveal which objective range give higher subjective rating, it is possible to define these preferred ranges with Neural Network (NN). The best data available is adopted from three drivers who tested 15 sedans, and some interesting results are found. The initial results demonstrate that NN is a powerful tool to uncover and graphically illustrate the links between objective metrics and subjective assessments, i.e., the specific range leading to better steering feel. Given a larger sample size, more reliable and optimal links can be defined by following the same method. These confirmed links would enable vehicle dynamics engineers to more effectively develop new vehicles with nearly perfect steering feel.

The load forecasting has become an important role in the operation of power system, and several models by using different techniques have been applied to solve these problems. In the literature, the linear regression models are considered as a traditional approach to predict power consumption, and more recently, the artificial neural network (ANN) models have received more attention for a great number of successful and practical applications. This report introduces both linear regression and ANN models to predict the power consumption for Fortum in Ekerö. The characteristics of power consumption of different kinds of consumers are analyzed, together with the effects of weather parameters to power consumption. Further, based on the gained information, the numerical models of load forecasting are built and tested by the historical data. The predictions of power consumption are focus on three cases separately: total power consumption in one year, daily peak power consumption during winter and hourly power consumption. The processes of development of the models will be described, such as the choice of the variables, the transformations of the variables, the structure of the models and the training cases of ANN model. In addition, two linear regression models will be built according to the number of input variables. They are simple linear regression with one input variable and multiple linear regression with several input variables. Comparison between the linear regression and ANN models will be carried out. In the end, it finds out that the linear regression obtains better results for all the cases in Ekerö. Especially, the simple linear regression outperforms in prediction of total power consumption in one year, and the multiple linear regression is better in prediction of daily peak load during the winter.

Master of Science / Department of Statistics / Weixin Yao / In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.

Background: The Active Commuting Route Environment Scale (ACRES) was developed to study active commuters’ perceptions of their route environments. The overall aims were to assess the measuring properties of the ACRES and study active bicycle commuters’ perceptions of their commuting route environments. Methods: Advertisement- and street-recruited bicycle commuters from Greater Stockholm, Sweden, responded to the ACRES. Expected differences between inner urban and suburban route environments were used to assess criterion-related validity, together with ratings from an assembled expert panel as well as existing objective measures. Reliability was assessed as test-retest reproducibility. Comparisons of ratings between advertisement- and street-recruited participants were used for assessments of representativity. Ratings of inner urban and suburban route environments were used to evaluate commuting route environment profiles. Simultaneous multiple linear regression analyses were used to assess the relation between the outcome variable: whether the route environment hinders or stimulates bicycle-commuting and environmental predictors, such as levels of exhaust fumes, speeds of traffic and greenery, in inner urban areas. Results: The ACRES was characterized by considerable criterion-related validity and reasonable test-retest reproducibility. There was a good correspondence between the advertisement- and street-recruited participants’ ratings. Distinct differences in commuting route environment profiles between the inner urban and suburban areas were noted. Suburban route environments were rated as safer and more stimulating for bicycle-commuting. Beautiful, green and safe route environments seem to be, independently of each other, stimulating factors for bicycle-commuting in inner urban areas. On the other hand, high levels of exhaust fumes and traffic congestion, as well as low ‘directness’ of the route, seem to be hindering factors. Conclusions: The ACRES is useful for assessing bicyclists’ perceptions of their route environments. A number of environmental factors related to the route appear to be stimulating or hindering for bicycle commuting. The overall results demonstrate a complex research area at the beginning of exploration. / BAKGRUND: Färdvägsmiljöer kan tänkas påverka människors fysiskt aktiva arbetspendling och därmed bidra till bättre folkhälsa. Studier av färdvägsmiljöer är därför önskvärda för att öka förståelsen kring möjliga samband mellan fysiskt aktiv arbetspendling och färdvägsmiljöer. En enkät, ”The Active Commuting Route Environment Scale” (ACRES), har därför skapats i syfte att studera fysiskt aktiva arbetspendlares upplevelser av sina färdvägsmiljöer. Huvudsyftet med denna avhandling var dels att studera enkätens psykometriska egenskaper i form av validitet och reliabilitet, dels att studera arbetspendlande cyklisters upplevelser av sina färdvägsmiljöer. METODER: Arbetspendlande cyklister från Stor-Stockholm rekryterades via tidningsannonsering och via direkt kontakt i anslutning till färdvägen. Deltagarna besvarade enkäten ACRES. Tillsammans med skattningar från en grupp av experter och redan existerande objektiva mått användes förväntade skillnader mellan färdvägsmiljöer i inner- och ytterstaden för att studera kriterierelaterad validitet. Reliabiliteten studerades som reproducerbarhet via upprepade mätningar (test-retest). Jämförelser mellan skattningar av deltagare rekryterade via annonsering och via direkt kontakt i färdvägsmiljöer användes för att studera representativitet. Skattningar av färdvägsmiljöer i inner- och ytterstaden användes vidare för att studera färdvägsmiljöprofiler. Multipel linjär regressionsanalys användes även för att studera sambandet mellan utfallsvariabeln huruvida färdvägsmiljön motverkar eller stimulerar arbetspendling med cykel och miljöprediktorer, såsom avgasnivåer, trafikens hastighet och grönska, i innerstadsmiljöer. RESULTAT: Enkäten ACRES visade god kriterierelaterad validitet och rimlig reproducerbarhet. Det var en god överrensstämmelse mellan skattningar av deltagare rekryterade via annonsering och via direkt kontakt. Färdvägsmiljöprofilerna visade tydliga skillnader mellan inner- och ytterstadsmiljöer. Ytterstadens färdvägsmiljöer skattades som tryggare och mer stimulerande för arbetspendling med cykel än innerstadens färdvägsmiljöer. Vidare verkar vackra, gröna och trygga färdvägsmiljöer, oberoende av varandra, vara stimulerade faktorer för arbetspendling med cykel i innerstadsmiljöer. Däremot verkar höga avgasnivåer, höga trängselnivåer och färdvägar som kräver många riktningsändringar vara motverkande faktorer. SLUTSATSER: Enkäten ACRES är ett användbart instrument vid mätningar av cyklisters upplevelser av sina färdvägsmiljöer. Ett antal faktorer relaterade till färdvägsmiljön verkar vara stimulerande respektive motverkande för arbetspendling med cykel. Generellt sett på visar resultaten ett relativt outforskat och komplext forskningsområde. / <p>Örebro universitet, Hälsoakademin</p> / FAAP

Coconuts are one of the most economically important plants in Mozambique, where millions of people depend on income from coconuts. The coconut lethal yellowing disease (CLYD) is a highly destructive disease that ever since the early 90’s causes coconut palms in Mozambique to stop producing fruit and leave the coconut farmers with only empty stems. This thesis examines the disease's effect on the farmers’ income, both from coconuts and other complementary sources, since the vendible harvest should decrease with the incidence of the disease. The method used is multivariate linear regression, where several income variables are used as dependent variables. Two models are created, one only interpreted for the sample of 488 observations and one aiming at generalizing the results. By this study, it cannot be confirmed that the incidence of CLYD has a significant effect on coconut farmers’ income. The results from the sample analysis do however show that the income is affected by the degree of the disease, which is an incentive for continued research in the field.