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Os modelos de regressão logística ordinais são usados para descrever a relação entre uma variável resposta categórica ordinal e uma ou mais variáveis explanatórias. Uma vez ajustado o modelo de regressão, se faz necessário verificar a qualidade do ajuste do modelo. As estatísticas qui-quadrado de Pearson e da razão de verossimilhanças não são adequadas para acessar a qualidade do ajuste do modelo de regressão logística ordinal quando variáveis contínuas estão presentes no modelo. Para este caso, foram propostos os testes de Lipsitz, a versão ordinal do teste de Hosmer-Lemeshow e os testes qui-quadrado e razão de verossimilhanças de Pulkistenis-Robinson. Nesta dissertação é feita uma revisão das técnicas de diagnóstico disponíveis para os Modelos logito cumulativo, Modelos logito categorias adjacentes e Modelos logito razão contínua, bem como uma aplicação a fim de investigar a relação entre a perda auditiva, o equilíbrio e aspectos emocionais nos idosos. / Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables which could be discrete or continuous. Once the regression model has been fitted, it is necessary to check the goodness-of-fit of the model. The Pearson and likelihood-ratio statistics are not adequate for assessing goodness-of-fit in ordinal logistic regression model with continuous explanatory variables. For this case, the Lipsitz test, the ordinal version of the Hosmer-Lemeshow test and Pulkstenis-Robinson chi-square and likelihood ratio tests were proposed. This dissertation aims to review the diagnostic techniques available for the cumulative logit models, categories adjacent logit models and continuous ratio logistic models. In addition, an application was developed in order to investigate the relationship between hearing loss, balance and emotional aspects in the elderly.

The purpose of this study is to examine the position and use of an upper extremity prosthetic simulator on non-amputees. To see how a 3D printed prosthetic simulator can be optimized to serve the user correctly and accurately. In addition, this study examines the improvement of the Hosmer 5X Prosthetic Hook with the addition of newly designed trusses on to the prosthetic, as well as utilizing a new manufacturing method known as 3D printing. These topics are important because there is no standardized prosthetic simulator for schools and research facilities to use. Off the shelf prosthetic simulator cost upwards of $2000, often too expensive for early stage research. By optimizing the Hosmer 5X Prosthetic Hook with 3D printing, this new opportunity could allow amputees, from a range of income classes, to have access to a wide variety of prosthetics that are strong enough to support everyday living activities. A low-cost prosthetic that is easily distributable and accessible can give people a chance to regain their independence by giving them different options of efficient prosthetic devices, without having to spend so much. The devices in this project were design and analyzed on SOLIDWORKS, 3D scanned on the Artec Space Spider, and surfaced on Geomagic Wrap. Key results include developing a low-cost, robust prosthetic simulator capable of operating a Hosmer 5X Prosthetic hook, as well as developing a lighter version of the Hosmer 5X Prosthetic Hook that is more cost efficient and easily obtainable to the population around the world.

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Approved for public release; distribution in unlimited. / In this thesis we develop a procedure for detecting erroneous payments in the Defense Finance Accounting Service, Internal Review's (DFAS IR) Knowledge Base Of Erroneous Payments (KBOEP), with the use of supervised (Logistic Regression) and unsupervised (Classification and Regression Trees (C & RT)) modeling algorithms. S-Plus software was used to construct a supervised model of vendor payment data using Logistic Regression, along with the Hosmer-Lemeshow Test, for testing the predictive ability of the model. The Clementine Data Mining software was used to construct both supervised and unsupervised model of vendor payment data using Logistic Regression and C & RT algorithms. The Logistic Regression algorithm, in Clementine, generated a model with predictive probabilities, which were compared against the C & RT algorithm. In addition to comparing the predictive probabilities, Receiver Operating Characteristic (ROC) curves were generated for both models to determine which model provided the best results for a Coincidence Matrix's True Positive, True Negative, False Positive and False Negative Fractions. The best modeling technique was C & RT and was given to DFAS IR to assist in reducing the manual record selection process currently being used. A recommended ruleset was provided, along with a detailed explanation of the algorithm selection process. / Lieutenant Commander, United States Navy

I den här uppsatsen beskrivs en ansats till att med hjälp av statistiska metoder förutse utfallet i förmögenhetsrättsliga dispositiva tvistemål. Logistiska- och multilogistiska regressionsmodeller skattades på data för 13299 tvistemål från 5 tingsrätter och användes  till att förutse utfallet för 1522 tvistemål från 3 andra tingsrätter.   Modellerna presterade bättre än slumpen vilket ger stöd för slutsatsen att man kan använda statistiska metoder för att förutse utfallet i denna typ av tvistemål. / BACKROUND: The idea of legal automatization is a controversial topic that's been discussed for hundreds of years, in modern times in the context of Law & Artificial Intelligence. Strangely, real world applications are very rare. Assuming that the judicial system is like any system that transforms inputs into outputs one would think that we should be able measure it and and gain insight into its inner workings and ultimately use these measurements to make predictions about its output. In this thesis, civil procedures on commercial matters amenable to out-of-court settlement (Förmögenhetsrättsliga Dispositiva Tvistemål) was devoted particular interest and the question was posed: Can we predict the outcome of civil procedures using Statistical Methods? METHOD: By analyzing procedural law and legal doctrin, the civil procedure was modeled in terms of a random variable with a discrete observable outcome. Some data for 14821 cases was extracted from eight different courts. Five of these courts (13299 cases) were used to train the models and three courts (1522 cases) were chosen randomly and kept untouched for validation. Most cases seemed to concern monetary claims (66%) and/or damages (12%). Binary- and Multinomial- logistic regression methods were used as classifiers. RESULTS: The models where found to be uncalibrated but they clearly outperformed random score assignment at separating classes and at a preset threshold gave accuracies significantly higher (p<<0.001) than that of random guessing and in identifying settlements or the correct type of verdict performance was significantly better (p<<0.003) than consequently guessing the most common outcome. CONCLUSION: Using data for cases from one set of courts can to some extent predict the outcomes of cases from another set of courts. The results from applying the models to new data concludes that the outcome in civil processes can be predicted using statistical methods.