EMSE Dissertation Public Defense
<p class="p1"> ANNOUNCEMENT OF PUBLIC DEFENSE OF A DOCTOR OF PHILOSOPHY DISSERTATION</p> <p class="p1"> Title: An Investigation into the Analysis of Truncated Standard Normal Distributions Using Heuristic Techniques</p> <p class="p1"> By: John Walter Ralls, a doctor of philosophy candidate in the Department of Engineering Management and Systems Engineering</p> <p class="p1"> When: Thursday, March 13, 2014 at 1:00 p.m.</p> <p class="p3"> <span class="s1">Where: KH247 (and at <a href="https://connect.odu.edu/odu_emse"><span class="s2">https://connect.odu.edu/odu_emse</span></a>)</span></p> <p class="p2"> </p> <p class="p1"> ABSTRACT</p> <p class="p1"> Standard normal distributions (SND) and truncated standard normal distributions (TSND) have been widely used and accepted methods to characterize the data sets in various engineering disciplines, financial industries, medical fields, management, and other mathematic and scientific applications. For engineering managers, risk managers and quality practitioners, the use of the standard normal distribution and truncated standard normal distribution have particular relevance when bounding data sets, evaluating manufacturing and assembly tolerances, and identifying measures of quality. In particular, truncated standard normal distributions are used in areas such as component assemblies to bound upper and lower process specification limits.</p> <p class="p1"> A heuristic approach for the analysis of assembly-level truncated standard normal distributions is developed. The research approach leverages the insight provided through the further analysis of varying truncated standard normal distributions using characteristic function inversion. Specifically, unique properties of a characteristic function are utilized as a method for the analysis of truncated assemblies. Two heuristic procedures are developed for the analysis of TSNDs (single and assembly based). Mathematical formulations are presented to provide decision makers and engineering managers an alternative approach to understanding TSND assemblies. Correlation and regression analysis is performed and provides insight into the relationship between the truncated standard normal distributions analyzed. The heuristic procedures and results presented have application to engineering managers and decision makers. The analysis approach will serve as a benchmark for future research.</p> <p class="p1"> For more information contact the faculty advisor: Dr. C. Ariel Pinto at <a href="mailto:cpinto@odu.edu"><span class="s3">cpinto@odu.edu</span></a></p>
Posted By: Cesar Pinto
Date: Mon Mar 10 11:47:57 EDT 2014