This research focuses on characterizing a potato with extra sugar content and identifying the location and depth of the extra sugar content using the active thermography imaging technique. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The inspection system and general methodologies characterizing extra sugar content will be presented in this study. The average heating rate obtained from the thermal image analysis is the major factor in characterization procedures. Using information on the average heating rate, the probability of achieving a potato with extra sugar content may be predicted using the logistic regression model. In addition, neural networks are also used to identify the potato with extra sugar contents. The correct rate for identifying a potato with extra sugar content in it can reach 85%. The location of extra sugar content can also be found using the logistic regression model. Results show the overall correct rate predicting the extra sugar content location with a resolution of 20 by 20 pixels is 91%. In predicting the extra sugar content depth, amounts exceeds 2/3 inches are not detectable by analyzing thermal images. The depth of extra sugar content can be discriminated in 0.3 inch increments with a high rate of accuracy (87.5%).

High quality defense personnel have decisive influence at modern war, and therefore it is the benefit for national security, and the root, garuantee for enhancing military combat power. For years, highly personnel resignation rate has been an important issue for militart personnel resources management. Abnormal resignation rate will not only influences the quality of organizational operation but also disr pts the experience of personnel of the organizational structure.Especially for military services,it will effect our national security and combat power as a whole. General studies of probing resignation were most focuset on factors of resignation will,tendency as probing issues,seldom studies were focused on systematic stuies of resignation rate. Therefore, it is a respond of human resources policies to probe resignation rate in an appropriate way. In this stay, the commissioned and non-commissioned offices in Chinese Marine Corp who stationed in southern Taiwan were taken as probing factors. The predictable capability of Logistic Regression Model has been used in this study as well in order to create the calculation model mode for resignanation rate. The result of the study has been comfirmed that educational level, part-time studies, seniority, marriage, ranking, branch of military services, salary, unit character, welfare and so on were all resignationrelared. Also it is acceptable to predict resignation rate by utilizing this method.

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The bypassing of rural hospitals increased in Colorado's rural communities during the 1990s. To understand this phenomenon, this study explores why rural Medicare patients in Colorado bypassed their local rural hospitals when they could have received health care services at their nearest local hospital. To identify both individual factors and institutional variables associated with hospital choice behavior, the conditional logistic regression model analyzes 4,099 rural Medicare patients who received heart failure and shock procedures. This study determines that both institutional variables (ownership type, number of beds, number of services, accreditation, and distance between the hospital and a patient's residence) and patient variables (age, length of stay, race, and total charge) are significant in patients' hospital choice. This study suggests that rural hospitals could build cooperative relationships with other large rural and urban hospitals.

Binary response data is often modeled using the logistic regression model, a well known nonlinear model. Designing an optimal experiment for this nonlinear situation poses some problems not encountered with a linear model. The application of several optimality design criteria to the logistic regression model is explored, and many resulting optimal designs are given. The implementation of these optimal designs requires the parameters of the model to be known. However, the model parameters are not known. If they were, there would be no need to design an experiment. Consequently the parameters must be estimated prior to implementing a design. Standard one-stage optimal designs are quite sensitive to parameter misspecification and are therefore unsatisfactory in practice. A two-stage Bayesian design procedure is developed which effectively deals with poor parameter knowledge while maintaining high efficiency. The first stage makes use of Bayesian design as well as Bayesian estimation in order to cope with parameter misspecification. Using the parameter estimates from the first stage, the second stage conditionally optimizes a chosen design optimality criterion. Asymptotically, the two-stage design procedure is considerably more efficient than the one-stage design when the parameters are misspecified and only slightly less efficient when the parameters are known. The superiority of the two-stage procedure over the one-stage is even more evident for small samples. / Ph. D.

<p> We developed and tested procedures to rank the performance of security analysts according to the timeliness of their earning forecasts. We compared leaders and followers among analysts on various performance attributes, such as accuracy, boldness, experience, brokerage size and so on. We also use discriminant analysis and logistic regression model to examine what attributes have an effect on the classification. Further, we examined whether the timeliness of forecasts is related to their impact on stock prices. We found that the lead analysts identified by the measure of forecast timeliness have a greater impact on stock price than follower analysts. Our initial sample includes all firms on the Institutional Brokers Estimate System (I/B/E/S) database and security return data on the daily CRSP file for the years 1994 through 2003.</p> / Thesis / Master of Science (MSc)

In the agricultural state of Iowa, water quality research is of great importance for monitoring and managing the health of aquatic systems. Among many water quality parameters, water temperature is a critical variable that governs the rates of chemical and biological processes which affect river health. The main objective of this thesis is to develop a real-time high resolution predictive stream temperature model for the entire state of Iowa. A statistical model based solely on the water-air temperature relationship was developed using logistic regression approach. With hourly High Resolution Rapid Refresh (HRRR) air temperature estimations, the implemented stream temperature model produces current state-wide estimations. The results are updated hourly in real-time and presented on a web-based visualization platform: the Iowa Water Quality Information System, Beta version (IWQIS Beta). Streams of 4th order and up are color-coded according to the estimated temperatures. Hourly forecasts for lead time of up to 18 hours are also available. A model was developed separately for spring (March to May), summer (June to August), and autumn (September to November) seasons. 2016 model estimation results generate less than 3 °C average RMSE for the three seasons, with a summer season RMSE of below 2 °C. The model is transferrable to basins of different catchment sizes within the state of Iowa and requires hourly air temperature as the only input variable. The product will assist Iowa water quality research and provide information to support public management decisions.

The Company often enlarge its scale to maintain its competitive advantage by investing. When company lacks of internal funds, it will raise funds from outside. The purpose of this study is to explore how company chooses financial instruments and influence of the announcement effect on stock price. This study analyzes Taiwan listed company by the the sample period from 1993 to 2005. There are two parts of the thesis. The first is the factor of choosing certain financial instrument. We use logistic regression model, both binary and multinomial, to figure it out. The second is the influence of the announcement effect has on the stock price. We use event study to find whether abnormal return exists. Conclusion: 1. If the company¡¦s size is larger, it will choose debt to raise funds. 2. If R&D expense relative to net sales, debt ratio, the proportion of intangible asset are higher, the company will be tend to raise funds by choosing convertible bond 3. If the stock price is overvalued, the company will choose stock. 4. Taiwan listed company will experience negative stock return whatever it chooses stock, debt, or convertible bond.

This thesis treats with the theory, interpretation and application of the Discrete Choice Models. The theoretical part contains the Fitting the Logistic Regression Model, Testing for the Significance of the Coefficients, Testing for the Significance of the Model. The Multiple Logistic Regression is mentioned too. The model was applied to interview data from the International research called Reflex.