The goal of this theses is to compare existing bankruptcy prediction models with its new modification unique for this work, which could perform better than its competition. Proposed model is logit-based and consists of the combination of variables used in Altman´s and Ohlson´s models. The final model is estimated for medium sized companies in EU which aren´t publicly traded. This model achieved prediction accuracy of 97,1% (97.4% for healthy and 91.1% for bankrupt compa-nies) on its original dataset. As expected, when verified on new dataset, the accu-racy dropped but still reaches 97.1% (99.3% for healthy and 37.7% for bankrupt companies). The model is compared with its competition (original and modified version of Ohlson´s and partially Altman´s models) and it is shown that it has higher prediction accuracy.

Energy production is associated with environmental impairment. Most anxious is the greenhouse gas emissions, which also arise from transportation. If battery electric vehicles should be able to alleviate the problem, they must be charged with environmentally friendly produced electricity. This paper investigates a possible relationship between battery electric vehicles and solar photovoltaic panels in household survey data from ENABLE.EU performed in ten European countries autumn 2017 – spring 2018. Estimated with a logit binary choice model, it is found that the probability that a household owns a battery electric vehicle increases if the household owns solar photovoltaic panels. Furthermore, this increase in probability is higher within countries with a higher market diffusion of battery electric vehicle and solar photovoltaic panels (France and the UK). This suggests that policy encouraging home charging of battery electric vehicles from solar photovoltaic panels that includes an energy storage facility could speed up the transition of the vehicle fleet.

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The Family Forest Research Center recently conducted a mail survey of about 1,400 Massachusetts landowners. Respondents were given questions about themselves and their land and were then asked to rate three carbon sequestration programs in terms of their likelihood to participate. An ordered logit model is used to estimate probabilities that landowners would participate in various improved forest management programs. There are several estimation issues to consider with the ordered logit model. The relative merits of alternative models, including the multinomial and binomial logit, rank-ordered logit, binary logit and mixed ordered logit are discussed. Results of the ordered logit indicate that older males with less education and who own less than 100 acres are less likely to participate in an improved forest management program. All landowners are less likely to participate in a program that requires a management plan and that has a lengthy time commitment, low revenue stream and early withdrawal penalty. Policy implications and direction for future research are discussed.

This study used focus groups and surveys to provide a comprehensive evaluation of the Regional Traveler Information Center (RTIC) at UMass Amherst. The evaluation was completed by obtaining the awareness, usage, and perceived effectiveness of RTIC’s information by residents in the Pioneer Valley. It was found that awareness of RTIC is limited due to its lack of advertisement. Usage is focused primarily on its webcams and advisory information. Surveys showed that participants perceive RTIC to be useful, even though they may never have seen the information before (the survey provided a chance for them to become familiar with the service). Revealed preference data were collected regarding the travelers' most memorable instances where real-time traffic information was provided. A binary logit model of a traveler's switch decision (route, departure time, mode, destination, trip cancellation, or combinations of them) with real-time traffic information was specified and estimated. It was found that travelers have an increasing tendency to switch away from the original option when the resulting delay caused by congestion increases. Receiving congestion and crash information also provided a tendency to take an alternative travel method. It was found that males tend to switch more often than females, and young individuals switch less often.

Water-recycling technologies have been developed to reduce water consumption and surface runoff in horticultural operations. However, WRT may increase risk of disease from water-borne pathogens such as Pythium and Phytophthora. More information is needed about producers' management practices and attitudes regarding irrigation runoff containment and recycling. A mail survey was administered in February 2013 to horticultural nursery growers in Virginia, Maryland, and Pennsylvania. Collected were respondents' demographic characteristics plus irrigation and disease management practices. The survey incorporated a choice experiment quantifying willingness to adopt water recycling given hypothetical disease outbreak, water shortage probabilities, and percentage cost increases via a conditional logit model. Two hundred and sixty respondents provide valuable insight into horticultural production in the Mid-Atlantic region. We were unable to calculate the implicit price of water or disease for adoption because the sample of 91 respondents for the choice experiment yielded a flat distribution of operations ranging in $100 to $7 million in nursery cost. However, findings did support the hypothesis that producers will be more likely to adopt selected WRT when cost decreases, probability of disease decreases. Only 33% chose to adopt. Cost is the biggest factor as the majority of producers are not equipped to handle water recycling or capture and would go out of business due to the expense. Disease is also significant factor inhibiting growers from adopting. Until mandatory environmental regulations in place to force producers to contain runoff, or until incentivized cost sharing programs are implemented, wide spread adoption of water recycling technologies is unlikely to occur. / Master of Science

A model that can be used to analyze both current and future high-speed rail corridors is presented in this work. This model has been integrated into the Transportation Systems Analysis Model (TSAM). The TSAM is a model used to predict travel demand between any two locations in the United States, at the county level. The purpose of this work is to develop tools that will create the necessary input data for TSAM, and to update the model to incorporate passenger rail as a viable mode of transportation. This work develops a train dynamics model that can be used to calculate the travel time and energy consumption of multiple high-speed train types while traveling between stations. The work also explores multiple options to determine the best method of improving the calibration and implementation of the model in TSAM. For the mode choice model, a standard C logit model is used to calibrate the mode choice model. The utility equation for the logit model uses the decision variables of travel time and travel cost for each mode. A modified utility equation is explored; the travel time is broken into an in-vehicle and out-of-vehicle time in an attempt to improve the model, however the test determines that there is no benefit to the modification. In addition to the C-logit model, a Box-Cox transformation is applied to both variables in the utility equation. This transformation removes some of the linear assumptions of the logit model and thus improves the performance of the model. The calibration results are implemented in TSAM, where both existing and projected high-speed train corridors are modeled. The projected corridors use the planned alignment for modeling. The TSAM model is executed for the cases of existing train network and projected corridors. The model results show the sensitivity of travel demand by modeling the future corridors with varying travel speeds and travel costs. The TSAM model shows the mode shift that occurs because of the introduction of high-speed rail. / Master of Science

Successfully implementing continuous improvement and quality methodologies have been challenging over the past century. Challenges occur in managing change in the organization, which can be effectively addressed through relationship building in concert with strong leadership. Even with solid leadership, change management, and relationship building, initiatives have challenges quantifying and identifying when to intervene in order to keep them on track for success. Creating a quantitative model using a 3-parameter logit s-curve, Blythe S-curve, to illustrate the growth and decay of the handoff of projects to the process owner will allow leadership the knowledge and direction to keep the initiative moving in a positive direction. Leveraging the Hotelling T2 multivariate charts on the residuals derived from the Blythe S-curve will give definitive statistical evidence on when to intervene on projects in the earliest possible time frame. Leaders in organizations implementing and actively using change management, relationship building, the Blythe S-curve model, and Hotelling T2 multivariate charts to drive quality and continuous improvement methodologies in their organization will improve the likelihood of success.

Between 1994 and 2003, two related concerns were in the educational spotlight. The first concern was participation rates of low income students in higher education. The second was the apparent disparity in Internet usage by low income and other disadvantaged individuals, highlighted in the report Falling Through The Net (United States Department of Commerce, 2000). The purpose of this study is to identify the economic factors and demographic attributes that influence participation of low income students in online distance learning courses offered by a Mississippi community college. This study centers on the hypothesis that there is no statistically significant difference between low income and non low income student participation rates in online distance education courses and that the economic factors, other than income, between low income participates and non low income participants will be statistically similar. Survey data collected from analyzed through the use of logistical regression to determine the relationship of demographic and economic factors to the decision to enroll in future online courses. It was found that students who are older and married are less likely to choose to enroll in future online distance learning courses. Students with higher numbers of courses completed and who paid for college with personal funds are more likely to enroll in future online distance learning courses than those with fewer numbers of distance learning hours completed and those who experience less difficulty traveling to campus are less likely to choose to enroll in future distance learning courses.

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