Master of Science / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / Gully erosion is a serious problem on military training lands resulting in not only soil erosion and environmental degradation, but also increased soldier injuries and equipment damage. Assessment of gully erosion occurring on Fort Riley was conducted in order to evaluate different gully location methods and to develop a gully prediction model based on logistic regression. Of the 360 sites visited, fifty two gullies were identified with the majority found using LiDAR based data. Logistic regression model was developed using topographic, landuse/landcover, and soil variables. Tests for multicollinearity were used to reduce the input variables such that each model input had a unique effect on the model output. The logistic regression determined that available water content was one of the most important factors affecting the formation of gullies. Additional important factors included particle size classification, runoff class, erosion class, and drainage class. Of the 1577 watersheds evaluated for the Fort Riley area, 192 watersheds were predicted to have gullies. Model accuracy was approximately 79% with an error of omission or false positive value of 10% and an error of commission or false negative value of 11%; which is a large improvement compared to previous methods used to locate gully erosion.

This thesis investigates the factors that are associated with annual changes in the share price of Johannesburg Stock Exchange (JSE) listed companies. In this study, an increase in value of a share is when the share price of a company goes up by the end of the financial year as compared to the previous year. Secondary data that was sourced from McGregor BFA website was used. The data was from 2004 up to 2011. Deciding which share to buy is the biggest challenge faced by both investment companies and individuals when investing on the stock exchange. This thesis uses binary logistic regression to identify the variables that are associated with share price increase. The dependent variable was annual change in share price (ACSP) and the independent variables were assets per capital employed ratio, debt per assets ratio, debt per equity ratio, dividend yield, earnings per share, earnings yield, operating profit margin, price earnings ratio, return on assets, return on equity and return on capital employed. Different variable selection methods were used and it was established that the backward elimination method produced the best model. It was established that the probability of success of a share is higher if the shareholders are anticipating a higher return on capital employed, and high earnings/ share. It was however, noted that the share price is negatively impacted by dividend yield and earnings yield. Since the odds of an increase in share price is higher if there is a higher return on capital employed and high earning per share, investors and investment companies are encouraged to choose companies with high earnings per share and the best returns on capital employed. The final model had a classification rate of 68.3% and the validation sample produced a classification rate of 65.2% / Mathematical Sciences / M.Sc. (Statistics)

Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, and each experiment was replicated 100 times. For both methods and all DIF types, a test length of 20 was sufficient for satisfactory DIF detection. The detection rate increased significantly with sample size for each method. With the ANOVA DIF method and uniform DIF, there was a difference in detection rates between discrimination parameter types, which favored varying discrimination and decreased with increased sample size. The detection rate of non-uniform DIF using the ANOVA DIF method was higher with fixed discrimination parameters than with varying discrimination parameters when relative underlying ability was unequal. In the combination DIF case, there was a three-way interaction among the experimental factors discrimination type, relative ability, and sample size for both detection methods. The error rate for the ANOVA DIF detection method decreased as test length increased and increased as sample size increased. For both methods, the error rate was slightly higher with varying discrimination parameters than with fixed. For logistic regression, the error rate increased with sample size when relative underlying ability was unequal between groups. The logistic regression method detected uniform and non-uniform DIF at a higher rate than the ANOVA DIF method. Because the type of DIF present in real data is rarely known, the logistic regression method is recommended for most cases.

This study aims to investigate the determinants of capital structure in small and medium-sized enterprises (SMEs) in Malaysia and their effect on firms’ performance. The study addresses the following primary question: What are the factors that influence the capital structure of SMEs in Malaysia? The sample of this research is SMEs in the east coast region of Malaysia. Adopting a positivist paradigm, the research design includes a preliminary study comprising 25 interviews with the owner-managers of SMEs, which is analysed using thematic analysis. The results are used to finalise the conceptual framework for the main study, which takes the form of a self-completion questionnaire survey. Usable responses were received from 384 firms, giving a response rate of 75.3%. The survey data is analysed using a series of binomial logistic regression models. Results reveal that there was no indication for the impact of owner’s education and experience on capital structure decisions. Other owner-related factors, firm characteristics, management performance and environment were found to relate to all types of capital structure. Both complete and partial mediating effects are also discovered in this study. The results provide evidence to support the pecking order hypothesis (Myers, 1984; Myers and Majluf, 1984), agency theory (Jensen and Meckling, 1976) and culture model of Schwartz (1994). It appeared that owner-managers in Malaysia do not strive to adjust their capital structure towards some optimal debt ratio, which is contrary to the static trade-off theory (DeAngelo and Masulis, 1980) of capital structure. This study makes several important contributions to the existing studies of capital structure. This research led to the development of a model of capital structure determinants by integrating factors related to owner-managers, firms, culture, and environment. This study incorporates methodological triangulation that may mitigate the problem of the difficulties in accessing financial data of SMEs in Malaysia. This study also provides meaningful insight into the financing preferences of the owner-managers with relevant implementations to academics, business practitioners, financial providers and policymakers. The research findings should assist owner-managers in making optimal capital structure decisions as well as help the policymaker in making an appropriate policy on the financing.

Logistic regression is a widely used statistical method in data analysis and machine learning. When the capacity of data is large, it is time-consuming and even infeasible to perform big data machine learning using the traditional approach. Therefore, it is crucial to come up with an efficient way to evaluate feature combinations and update learning models. With the approach proposed by Yang, Wang, Xu, and Zhang (2018) a system can be represented using small enough matrices, which can be hosted in memory. These working sufficient statistics matrices can be applied in updating models in logistic regression. This study applies the working sufficient statistics approach in logistic regression machine learning to examine how this new method improves the performance. This study investigated the difference between the performance of this new working sufficient statistics approach and performance of the traditional approach on Spark\rq s machine learning package. The experiments showed that the working sufficient statistics method could improve the performance of training the logistic regression models when the input size was large.

This project analyzed the giving data of Worcester Polytechnic Institute's alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due to the nature of the data, multiple imputation was performed on the missing information to validate generalization of the models to a broader population. Concepts from the field of direct and database marketing, like "score" and "lift", were also introduced in this report.

A inclusão da eletroencefalografia nos protocolos de pesquisa diagnóstica para DA é plenamente justificada por sua larga disponibilidade, baixo custo, alta sensibilidade, o que possibilita a realização de exames seriados e o acompanhamento da evolução do estudo neurológico. Objetivo: Determinar um índice de corte, para utilizaçào na prática clínica, no auxilio diagnóstico da doença de Alzheimer. Metodologia: Avaliamos dois grupos de indivíduos compostos por 57 voluntários normais e idade superior a 50 anos comparados a 50 indivíduos com DA provável. Realizamos registros de EEG em vigília, olhos fechados e repouso por 30 minutos e computamos as potências espectrais das bandas de frequência alfa e teta, para todos os eletrodos, e calculamos a razão alfa/teta. Realizamos a regressão logística das variáveis razão alfa/teta da potência média do eletrodo C3 e do eletrodo O1e calculamos uma fórmula para o auxílio no diagnóstico da DA com um acerto cuja, sensibilidade para DA de 76, 4 % e especificidadede 84,6 % e a área sob a curva ROC 0.92. Conclusão: A regressão logística da razão alfa/teta do Espectro da potência média do EEG é um bom marcador para discriminar pacientes com doença de Alzheimer de controles normais / The inclusion of electroencephalography in diagnostic research protocols for AD is fully justified given EEG\'s wide availability, low cost and high sensitivity, allowing serial exams and neurological evolution follow-ups. Objective: To determine a screening index for use in routine clinical practice to aid the diagnosis of Alzheimer\'s disease. Methodology: Two groups of individuals older than 50 years, comprising a control group of 57 normal volunteers and a study group of 50 patients with probable AD, were compared. EEG recordings were performed of subjects in a wake state with eyes closed at rest for 30 mins. Spectral potentials of the alpha and theta bands were computed for all electrodes and the alpha/theta ratio calculated. Logistic regression of the variables alpha/theta of the mean potential of the C3 and O1 electrodes was carried out. A formula was calculated to aid the diagnosis of AD yielding 76.4 % sensitivity and 84.6 specificity for AD with an area under the ROC curve of 0.92. Conclusion: Logistic regression of the alpha/theta of the Spectrum of the mean potential of EEG represents a good marker for discriminating between AD patients and normal controls

Yiu Tat-choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 68-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Student Ratings of Instructors --- p.2 / Chapter 1.2 --- Research Plan and Difficulties Encountered in the Study --- p.4 / Chapter 2 --- Data and An Overall Picture of Study --- p.7 / Chapter 2.1 --- The Questionnaire and Data Collection Method --- p.7 / Chapter 2.2 --- Pilot Study --- p.8 / Chapter 2.3 --- Data Editing --- p.12 / Chapter 2.3.1 --- Clerical Error --- p.12 / Chapter 2.3.2 --- Strange Patterns --- p.13 / Chapter 2.4 --- Missing Items ´ؤ Item Nonresponse --- p.14 / Chapter 2.5 --- Missing Items - Unit Nonresponse --- p.16 / Chapter 2.6 --- Effective Class Size --- p.21 / Chapter 2.7 --- Imputation of Item Nonresponse Data --- p.23 / Chapter 2.8 --- Overall Picture of Study --- p.25 / Chapter 3 --- Data Analysis I: Logistic Regression --- p.28 / Chapter 3.1 --- Conditional Independence --- p.29 / Chapter 3.2 --- Partial Correlation --- p.30 / Chapter 3.3 --- Simultaneous p-value --- p.31 / Chapter 3.4 --- Logit Model --- p.32 / Chapter 3.5 --- Logit Model for Ordinal Variables --- p.35 / Chapter 3.6 --- Iteratively Reweighted Least Squares (IRLS) Algorithm --- p.36 / Chapter 3.7 --- Criteria for Assessing Model Fit --- p.38 / Chapter 3.7.1 --- Assessing the Fit of the Model --- p.39 / Chapter 3.7.2 --- Pearson Chi-Square and Deviance --- p.40 / Chapter 3.8 --- Interpretation of the Coefficients of The Weighted Logistic Re- gression Model --- p.42 / Chapter 3.8.1 --- Nominal Independent Variable --- p.42 / Chapter 3.8.2 --- Continuous Independent Variable --- p.45 / Chapter 4 --- Data Analysis II: Adjusted Instructor Score --- p.49 / Chapter 4.1 --- Removing Effects of Class Characteristics Factor and Adjust- ing the Score --- p.50 / Chapter 4.2 --- Adjusted Instructor Score (AIS) --- p.54 / Chapter 4.3 --- Estimate Standard Error of AIS by Bootstrap Method --- p.55 / Chapter 5 --- Conclusion --- p.58 / Chapter 5.1 --- Comparison Between the AIS and Average Score --- p.58 / Chapter 5.2 --- Discussion --- p.60 / Appendix A1: Course Evaluation Survey Form --- p.63 / Appendix A2: Course Evaluation Supplementary Form . --- p.64 / Appendix B: Descriptive Statistics for Response Rate --- p.65 / Appendix C: The Descriptions of Class Characteristics Dummy Variables --- p.67 / Bibliography --- p.68

Transfer models provide a viable means of determining which skills a student needs in order to solve a given problem. However, constructing a good fitting transfer model requires a lot of trial and error. The main goal of this thesis was to develop a procedure for developing better fit transfer models for intelligent tutoring systems. The procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from the instances in the dataset that the transfer model predicts incorrectly. The association rules found in the mining process determines what operation to perform on the current transfer model. Our search algorithm using association rules was compared to a blind search method that finds all possible transfer models for a given set of factors. Our search process was able to find statistically similar models to the ones the blind search method finds in a considerably shorter amount of time. The difference in times between our search process and the blind search method is days to minutes. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.

A central topic in empirical asset pricing is how to explain anomalies in various trading horizons. This dissertation contains two essays that study several anomalies in medium-term/long-term investment in the equity market and in high-frequency trading in the foreign exchange market. In the first essay, I propose an investor underreaction model with heterogeneous truncations across time and stocks. In this setting, investors are more attracted to dramatic changes in stock prices than to gradual changes. Continuous information causes signals to be truncated which delays their incorporation into stock prices thus generating momentum. Under the assumption that investors are more attracted to winner stocks and ignore more information in loser stocks, I show that a loser portfolio exhibits stronger momentum and higher profitability than a winner portfolio with the same discreteness level. A trading strategy based on this model yields high alphas and Sharpe ratios. Evidence from social media trends aligns well with this model. In the second essay, I develop multivariate logistic models to explain the short-term offer price movement of the currency pair EUR/USD from the EBS limit order book. Using logistic regression based methods, I study the impact of various market microstructure factors on offer price changes in the next second. The empirical results show explanatory power for the testing sample up to 45% and a true positive rate of the prediction up to 87%. The model reveals interesting mechanisms for the underlying driving forces of the tick-by-tick currency price movement.