In vehicles one of the most important components which affect comfort of the driver and the purchasing decision is the driver&rsquo / s seat. In order to improve design of a driver seat in a leader company of automotive sector, a comprehensive analysis of customer expectations from the driver seat is performed with a cross functional team formed by representatives of design, marketing, production, quality and services departments. In this study, collection of customer voice data and development of an exceptional &ldquo / customer satisfaction estimation model&rdquo / using these data are presented. Data are modeled by the help of Logistic Regression. This model is able to estimate how much a given customer is likely to be satisfied with the driver seat at a certain confidence level. It is also explained how this model can be used to identify design improvement opportunities that help increase the probability that a customer likes the driver seat. The modeling and analysis approach used for the particular case is applicable in general to many other cases of product improvement or development.

Infant mortality rate is used as an indicator of a nation&rsquo / s economic welfare. Despite the tremendous reduction since 1900s infant mortality rate is still high for developing countries. Infant mortality is reduced from 67 to 21 per 1000 live births in 17 years from 1990 to 2007 in Turkey. However, IMR in Turkey is still much higher than the rates in developing countries which is reported as 5 in 2007. In this thesis, I examine regional, household and individual level characteristics that are associated with infant mortality. For this purpose survival analysis is used in this analysis. The data come from 2003-2004 Turkey Demographic and Health Survey that includes detailed information of 8,075 ever married women between the ages 15-49. 7,360 mothers of these women gave birth to 22,443 children. The results of the logistic regression show that intervals between the births of the infants are associated with infant mortality at lower levels of wealth index. Children from poorer families with preceding birth interval shorter than 14 months or children whose mothers experience a subsequent birth fare badly. Breastfeeding is important for the survival chance of the infants under the age 3 months. Place of delivery and source of water the family uses are also found to be correlated with infant mortality risk. Curvilinear relation between maternal age at birth and infant mortality risk is observed, indicating higher risk for teenage mothers and mothers having children at older ages.

ABSTRACT PREDICTION OF LITIGATION PROBABILITY FOR INTERNATIONAL CONSTRUCTION PROJECTS DURING BIDDING STAGE Ayten, ilkay M.S., Department of Civil Engineering Supervisor: Assoc. Prof. Dr. Rifat S&ouml / nmez February 2010, 102 pages Over the years many researchers agreed that between the parties involved in construction projects such as / owner, contractor, engineer and suppliers trying to perform different scopes in different timetables. Therefore, disputes are inevitable due to the complexity of the work. Occurrence of litigation is the most terrifying process to deal with during any construction project for both owner and the contractor because of the time and money consuming nature of the process. Hence, contractors should try to eliminate any potential risk factors that will lead to litigation. The aim of this study is to investigate the factors that influence court action between parties in international construction projects and develop a statistical model that will predict the litigation probability of an international construction project during bidding stage. The final prediction model revealed that contractual awareness and consciousness of risk factors is the key to predict litigation probability. Considering awareness of the factors affecting litigation probability are displayed in this thesis. Companies may have the opportunity to develop risk assessment and management strategies while reconsidering their contingency estimates.

Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the optimal forecast values. In this simulation, 21 different methods, including na&iuml / ve and complex ones, are used by the help of R software. It is concluded that transition models and random effects models with no lag of response can be chosen for getting the most accurate forecasts, especially for the first two years of forecasting.

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Man-made farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing and bring with it the loss of this refuge function. It is ecologically significant because one fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon-Wiener diversity index (H’). Results indicate that ponds with regular shape or the ones with larger size possess a strong positive correlation with H’. Farm ponds adjacent to farmland benefited waterside bird diversity. On the other hand, urban development was shown to cause the reduction of farmland and pond numbers, which in turn reduced waterside bird diversity. By running the ANN model with four neurons, the resulting H’ index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28). Analysis of historical pond occurrence to the present showed that ponds with larger size and a long perimeter were less likely to disappear. Smaller (< 0.1 ha) and more curvilinear ponds had a more drastic rate of disappearance. Based on this finding, a logistic regression was constructed to predict pond-loss likelihood in the future and to help identify ponds that should be protected. Overlaying results from ANN and form logistic regression enabled the creation of pond-diversity maps for these simulated scenarios of development intensities with respective to pond-loss trends and the corresponding dynamics of bird diversity.

Knowledge of fire risk is crucial for manufacturers and regulators to make correct choices in prescribing fire protection systems, especially flame retardants. Methods of determining fire risk are bogged down by a multitude of confounding factors, such as population demographics and overlapping fire protection systems. Teasing out the impacts of one particular choice or regulatory change in such an environment is crucial. Teasing out such detail requires statistical techniques, and knowledge of the field is important for verifying potential methods. Comparing the fire problems between two states might be one way to identify successful approaches to fire safety. California, a state with progressive fire prevention policies, is compared to Texas using logistic regression modeling to account for various common factors such as percentage of rural population and percentage of population in ‘risky’ age brackets. Results indicate that living room fires, fires in which the first item ignited is a flammable liquid, piping, or filter, and fires started by cigarettes, pipes, and cigars have significantly higher odds of resulting in a casualty or fatality than fires started by other areas of origin, items first ignited, or heat sources. Additionally, fires in Texas have roughly 1.5 times higher odds of resulting in casualties than fires in California for certain areas of origin, items first ignited, and heat sources. Methods of estimating fire losses are also examined. The potential of using Ramachandran’s power-law relationship to estimate fire losses in residential home fires in Texas is examined, and determined to be viable but not discriminating. CFAST is likewise explored as a means to model fire losses. Initial results are inconclusive, but Monte Carlo simulation of home geometries might render the approach viable. / text

The study examined school district expenditures in Texas and their correlations with student achievement. The following research question guided this study: Which resource allocations produce statistically significant correlations between the resource allocation variances among school district and student achievement? An ordinal logistic regression analysis included 1009 school districts in the State of Texas, 18 of 26 possible finance function codes provided per-pupil dollar amounts, and 9 of 11 possible demographic categories were utilized for the study. The study held the school district as the unit of analysis. The statistical model was used to regress the dollar amounts categorized by financial function codes and percent student demographics to determine if a relationship existed with the dependent variable of the Texas Education Agency’s defined accountability rating during the 5-year time period—2004-2008. At the national level, there is a long-standing debate over whether the amount of money allocated to education affects student achievement. The literature review presents two sides of the debate concerning whether financial resources make a difference with regard to student achievement as represented through district-level accountability ratings. The research revealed that specific school district resource allocations by function code are statistically significant with regard to district level accountability measures through the Texas Education Agency (TEA) accountability system. However, the odds ratios temper the impact of the significance. The research also revealed that demographics are statistically significant in the State of Texas accountability system. / text

The time spent searching for a parking space increases air pollution, driver frustration, and safety problems impacting among other issues, traffic congestion and as consequence the environment. In the United States, parking represents a $20 billion industry (National Parking Association, 2005), and research shows that a car is parked on average 90 percent of the time. To alleviate this problem, more parking facilities should be built or intelligent models to better utilize current facilities should be explored. In this thesis, a general methodology is proposed to provide solutions to the parking problem. First, stated preference data is used to study drivers' choice/behavior. Parking choices are modeled as functions of arrival time, parking price, age, income and gender. The estimated values show that choice is relatively inelastic with respect to distance and more elastic with respect to price. The data is used to estimate the price elasticity that induces drivers to change their behavior. Second, neural networks are used to predict space availability using data provided by a parking facility. The model is compared with traditional forecasting models used in revenue management. Results show that neural networks are an effective tool to predict parking demand and perform better than traditional forecasting models. Third, the price elasticity that induces drivers to change their choice or behavior is determined. Finally, taking as an input the forecasting results obtained from the neural network and the price elasticity, parking spaces are optimally allocated at different price levels to optimize facility utilization and increase revenue. This research considers a parking facility network consisting of multiple parking lots with two, three and four fare classes and utilizes revenue management techniques as a mean to maximize revenue and to stimulate and diversify demand. The output indicates the number of parking spaces that should be made available for early booking to ensure full utilization of the parking lot, while at the same time attempting to secure as many full price parking spaces to ensure maximization of revenue.

The purpose of this study was to investigate the relationship between cigarette smoking and socio-demographic variables based on the empirical literature and the primitive theories in the field. Two regression approaches, logistic regression and linear multiple regression, were conducted on the two most recent Arizona Adult Tobacco Surveys to test the hypothesized models. The results showed that cigarette smokers in Arizona are mainly residents who have not completed a four-year college degree, who are unemployed, White, non-Hispanic, or young to middle-aged adults. Among the socio-demographic predictors of interest, education is the most important variable in identifying cigarette smokers, even though the predictive power of these socio-demographic variables is small. Practical and methodological implications of these findings are discussed.