published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

Narrative data enable evaluators to understand other people's viewpoints without predetermining those viewpoints by using preselected questionnaire categories. Narrative data yield rich detail, insight, and information. However, reducing narrative data into meaningful conclusions is difficult and time consuming, and requires attention, commitment, and skill on the part of trained coders. The personal and situational characteristics of the coders (called value inertia and cognitive limitation biases) affect data reduction. The effects of coder exposure to expected project outcomes and the level of coder research methodology sophistication were investigated. Coders considered either sophisticated or naive in research methodology were exposed to positive, ambiguous, or negative project outcome expectations. The coders reduced, or categorized, 25 open-ended interview response sets into previously established positive, negative, and ambiguous statement-type, content-code categories. The effectiveness of coder training was also explored by computing generalizability (reliability) coefficients. High generalizability coefficients were found, regardless of level of exposure to project outcome expectations. This indicates that coders were able to code the same statements the same way and is an indication of the coders' ability to reproduce the results. Results of this study also indicate that evaluators should use sophisticated coders for the reduction of narrative data, given that option. Sophisticated coders appear more resistant to the effect of exposure to project outcome expectations, coding narrative data more positively with less variability than naive coders when exposed to positive outcome expectations.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / The current F/A-18 data reduction/analysis system is incapable of meeting increased customer demands. A new system has been developed and is based on new technologies. In the process of developing the new system, the design team had to divorce themselves from the current system and consider what the ideal system would consist of. This was accomplished with great success in the areas of timeliness of data turn around, customer satisfaction, and increased efficiency.

The problem addressed is absence of a class of objects in a finite set of objects, which is investigated by considering absence of a species and absence in relation to a threshold. Regarding absence of a species, we demonstrate that the assessed probability of absence of the class of objects in the finite set of objects given absence of the class in the sample is either exactly or approximately equal to the probability of observing a specific single object from the class of objects given the protocol for observation, where probability is interpreted as a degree of belief. Regarding absence in relation to a threshold, we develop a new estimator of the upper confidence bound for the finite population distribution function evaluated at the threshold and investigate its properties for a set of finite populations. In addition we show that estimation regarding the initial ordered value in the finite population has limited usefulness. / Graduation date: 1998

No description available.

Thesis (M. Phil.)--University of Hong Kong, 2005 . / Title proper from title frame. Also available in printed format.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The desire to acquire large quantities of high speed vibration and acoustic data during aircraft testing is usually satisfied through on-board high speed recording methods. However there is often a need to have some of this data instantly available at the ground station for flight safety and other purposes. A Data Processor (DP) has been developed allowing an airborne data acquisition system to acquire large amounts of wideband analog data, process the data in real-time, and develop reduced bandwidth information from high bandwidth channels. The reduced data can be inserted into a Pulse Code Modulation (PCM) stream and telemetered via a Radio Frequency (RF) link with a potential for a 2000:1 reduction in bandwidth. This on-board processing capability also lends itself to additional tasks such as the generation of a reduced bandwidth marker channel which can flag critical time periods of data activity. This flagging technique can be used to facilitate ground station analysis of specific segments of data, resulting in significant cost and time savings.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / This paper presents a case study of an FTI system with complex requirements in terms of the data acquisition, recording, and post-analysis. Gigabit Ethernet was the technology of choice to facilitate such a system. Recording in a Gigabit Ethernet environment raises a fresh challenge to perform fast data reduction and data mining for post-flight analysis. This paper describes the Quick Access Recorder used in this system and how it addresses this challenge.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A practical description is presented of the methods used to reduce spacecraft telemetry data using a hierarchial toolkit of software programs developed for a UNIX environment.

This thesis presents a methodology for data aggregation for capacity management. It is assumed that there are a very large number of products manufactured in a company and that every product is stored in the database with its standard unit per hour and attributes that uniquely specify each product. The methodology aggregates products into families based on the standard units-per-hour and finds a subset of attributes that unambiguously identifies each family. Data reduction and classification are achieved using well-known multivariate statistical techniques such as cluster analysis, variable selection and discriminant analysis. The experimental results suggest that the efficacy of the proposed methodology is good in terms of data reduction.