Each year, billions of dollars are spent on missiles and munitions by the United States government. It is therefore vital to have a dependable method to estimate the reliability of these missiles. It is important to take into account the age of the missile, the reliability of different components of the missile, and the impact of different launch phases on missile reliability. Additionally, it is of importance to estimate the missile performance under a variety of test conditions, or modalities. Bayesian logistic regression is utilized to accurately make these estimates. This project presents both previously proposed methods and ways to combine these methods to accurately estimate the reliability of the Cruise Missile.

When approaching a new research problem, we often use screening designs to determine which factors are worth exploring in more detail. Before exploring a problem, we don't know which factors are important. When examining a large number of factors, it is likely that only a handful are significant and that even fewer two-factor interactions will be significant. If there are important interactions, it is likely that they are connected with the handful of significant main effects. Since we don't know beforehand which factors are significant, we want to choose a design that gives us the highest probability a priori of being able to estimate all significant main effects with their associated two-factor interactions. This project examines the methodology of finding designs that do not rely on an assumed model. We propose a method of modifying the D-Optimality criteria that averages over models with a common set of main effects and varying subsets of two-factor interations. We also calculate the proportion of the subsets that produce estimable designs. We use these results to find the best models for given run size and number of main effects.

Graduate students in the Brigham Young University Chemistry Department are working to develop a filtering device that can be used to separate substances into their constituent parts. The device consists of a monomer and water mixture that is polymerized into a monolith inside of a capillary. The ideal monolith is completely solid with interconnected pores that are small enough to cause the constituent parts to pass through the capillary at different rates, effectively separating the substance. Although the end objective is to minimize pore sizes, it is necessary to first identify an experimental region where any combination of input variables will consistently yield homogeneous monoliths capable of flow. To accomplish this task, an experimental mixture design is used to model the relationship between the variables related to the creation of the monolith and the probability of creating an acceptable polymer. The results of the mixture design suggest that, inside of the constrained experimental region, mixtures with higher proportions of monomer and surfactant, low amounts of initiator and salt, and DEGDA as the monomer have the highest probability of producing a workable monolith. Confirmatory experiments are needed before future experimentation to minimize pore sizes is performed using the refined constrained experimental region determined by the results of this analysis.

Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data is proposed and its performance is demonstrated under three different scenarios using data from the 1997 National Labor Survey.

A child's parents are the primary source of knowledge and learned behaviors for developing children, and the benefits or repercussions of certain parental practices can be long lasting. Although parenting practices affect behavioral outcomes for children, families tend to be diverse in their circumstances and needs. Research attempting to ascertain cause and effect relationships between parental influences and child behavior can be difficult due to the complex nature of family dynamics and the intricacies of real life. Structural equation modeling (SEM) is an appropriate method for this research as it is able to account for the complicated nature of child-parent relationships. Both Frequentist and Bayesian methods are used to estimate the effect of latent parental behavior variables on child aggression and anxiety in order to allow for comparison and contrast between the two statistical paradigms in the context of structural equation modeling. Estimates produced from both methods prove to be comparable, but subtle differences do exist in those coefficients and in the conclusions to which a researcher would arrive. Although model estimates between the two paradigms generally agree, they diverge in the model selection process. The mother's behaviors are estimated to be the most influential on child aggression, while the influence of the father, socio-economic status, parental involvement, and the relationship quality of the couple also prove to be significant in predicting child aggression.

Understanding the possible mechanisms of gene transcription regulation is a primary challenge for current molecular biologists. Identifying transcription factor binding sites (TFBSs), also called DNA motifs, is an important step in understanding these mechanisms. Furthermore, many human diseases are attributed to mutations in TFBSs, which makes identifying those DNA motifs significant for disease treatment. Uncertainty and variations in specific nucleotides of TFBSs present difficulties for DNA motif searching. In this project, we present an algorithm, XPRIME-EM (Eliciting EXpert PRior Information for Motif Exploration using the Expectation-Maximization Algorithm), which can discover known and de novo (unknown) DNA motifs simultaneously from a collection of DNA sequences using a modified EM algorithm and describe the variation nature of DNA motifs using position specific weight matrix (PWM). XPRIME improves the efficiency of locating and describing motifs by prevent the overlap of multiple motifs, a phenomenon termed a phase shift, and generates stronger motifs by considering the correlations between nucleotides at different positions within each motif. Moreover, a Bayesian formulation of the XPRIME algorithm allows for the elicitation of prior information for motifs of interest from literature and experiments into motif searching. We are the first research team to incorporate human genome-wide nucleosome occupancy information into the PWM based DNA motif searching.

Within statistical methods, design of experiments (DOE) is well suited to make good inference from a minimal amount of data. Two types of designs within DOE are screening designs and optimization designs. Traditionally, these approaches have been necessarily separated by a gap between the objectives of each design and the methods available. Despite being so separated, in practice these designs are frequently connected by sequential experimentation. In fact, from the genesis of a project, the experimentor often knows that both designs will be necessary to accomplish his objectives. Due to advances in the understanding of experimental designs with complex aliasing and their analysis, a current topic within statistics is how to desegregate these methods into a more unified and economical approach. This project is one treatment of that issue.

It has been observed that not all L.D.S. Seminary students are active in church attendance. There is little doubt in the minds of educators that parents affect a great deal the activities of their children.The purpose of the study was to learn what relationship, if any, existed between a seminary student's church attendance and the church activities of his parents, and to find out which, if any, of five specific areas of parental church activity had the most positive effect on a student's church attendance.Based on his experience in the church, the writer chose the following areas of church activity as the ones most likely to affect a seminary student's church attendance,1. the parents have been married in the temple.2. the parents hold a church position.3. the parents have served a regular mission.4. the parents have served a stake mission.5. the parents attend church regularly.Information cards were sent to every seminary in the church and were completed by approximately 60,000 students. A 5 per cent stratified sample was used in tabulating the data for the study. These cards were designed to obtain information about the church attendance of the seminary students and the church activities of their parents.

The purpose of this study was to identify factors related to attitudes of teenagers toward the Family Home Evening Program of the Church of Jesus Christ of Latter-day Saints. Hypotheses involving the relationships of certain factors to attitudes toward home evening were tested. A cluster sample of twenty-three ninth grade classes was selected and data were gathered through use of a questionnaire. Statistics used in analysis of data included the Pearsonian product moment correlation coefficient, and analysis of variance. Responses to open-end questions were content analyzed by three independent judges. Findings suggest that perceived participation by teenagers in planning and presenting home evening lessons is related to positive attitudes toward the program. Teenagers' perception of their family as discussing questions or problems of importance to them during home evening also seems to be related to positive attitudes. No significant relationship was found between teenagers' perception of relative differences in task-maintenance orientations of their parents and teenagers' attitudes toward home evening.

This study was designed to examine whether religiosity is a determining factor in public reaction to obscenity issues. A sample population of 452 residents of the community of Idaho Falls, Idaho was selected using a technique based on a list of random numbers in October, 1979. Path analysis, Chi-square analysis and Spearman's correlation were used to measure the results. The study found there is a significant relationship between religiosity and behavior intention toward obscenity issues. The affective component of the attitude organization functions somewhat as a mediating variable. There were also findings that members of the Church of Jesus Christ of Latter-day Saints were significantly more concerned about such issues and intended to be more active in influencing them than other memebers of the community.