The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 18-25 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents.

Abstract-Companies are viewing customers in terms of their lifetime duration. Customer lifetime duration is a powerful and straightforward measure that synthesizes churn (attrition) risk at individual customer level. For existing customers, customer lifetime duration can help companies develop customer loyalty and treatment strategies to maximize customer value. In this study, based on the Kumaraswamy distribution, the Kumaraswamy Lindley distribution is studied. Some mathematical properties of Kumaraswamy Lindley distribution such as moments, hazard function, quantile function, skewness, kurtosis are derived. The method of maximum likelihood is used to estimate the model parameters and the observed information matrix is derived. An application of our results is provided to show the applicability of this distribution, especially for customer lifetime duration. Therefore, the proposed distribution can be a useful tool to analyze customer lifetime duration in marketing research.

This paper provides a new exponential type estimator in simple random sampling for population mean. It is shown that proposed exponential type estimator is always more efficient than estimators considered by Bahl and Tuteja (1991) and Singh, Chauhan, Sawan, and Smarandache (2009). From numerical examples it is also observed that proposed modified ratio estimator performs better than existing estimators.

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