To research the interaction between the elements of the traffic safety system “Traffic Participant – Vehicle – Road (Traffic Environment)” and the influencing factors, and to provide recommendations for the improvement of traffic safety in Lithuania. The following main problems were solved in the research: • to research the models applied for the analysis of traffic safety system; • to identify the key factors influencing the traffic safety system reliability; • to perform reaction tests of traffic participants, during which the driver’s reaction time in usual situations and in potentially dangerous or unforeseen situations is identified, i.e. while talking on a mobile phone, distractions, headlight dazzle at night, and other situations; • based on mathematical calculation methods and the tests performed, to identify the factors influencing the driver’s reaction time; • based on traffic accident data on certain main roads and by applying statistical mathematical packages, to identify the factors affecting the number of traffic accidents; • after analyzing the factors affecting the traffic safety system, to formulate substantiated trends for the improvement of traffic safety and to implement that traffic safety improvement means for these trends; • to assess the efficiency of traffic safety improvement means.

The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed for detecting single and multiple change-points, and their performance is compared by simulation studies. The proposed method is illustrated using sea surface temperature data and the tail distributions before and after the change-point from two models, with and without random effects are compared.

Master of Science / Department of Psychological Sciences / Patrick Knight / The current study examined attributions of blame for workplace errors through the lens of Kelley’s (1967) ANOVA model of attribution-making, which addresses the consensus, consistency, and distinctiveness of a behavior. Consensus and distinctiveness information were manipulated in the description of a workplace accident. It was expected that participants would make different attributions regarding the cause of the event due to these manipulations. This study further attempted to determine if an individual’s core self-evaluations (CSE) impact how she or he evaluates a workplace accident and attributes blame, either from the perspective of the employee who made the error or that of a co-worker. Because CSE are fundamental beliefs about an individual’s success, ability, and self-worth, they may contribute to how the individual attributes blame for a workplace accident. It was found that CSE were positively related to participants’ inclination to make internal attributions of blame for a workplace error. Contrary to expectations, manipulations of the consensus and distinctiveness of the workplace error did not moderate participants’ attributions of blame. Explanations for these findings are discussed, as are possible applications of this research.