Prediction intervals for reliability growth models with small sample sizes

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building, data collection, data analysis and data interpretation form the core of sound engineering practice.After the data has been gathered the engineer must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he or she must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty. In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organisations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering processes.The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved.
LanguageEnglish
Title of host publicationSpringer Handbook of Engineering Statistics
Place of PublicationHeidelberg
PublisherSpringer-Verlag
Pages113-124
Number of pages12
ISBN (Print)9781852338060
Publication statusPublished - 2006

Fingerprint

Reliability Growth
Prediction Interval
Small Sample Size
Growth Model
Engineering
Continuous Improvement
Data analysis
Statistics
Uncertainty
Development Process
Monitor
Manufacturing
Integrate
Growth model
Sample size
Prediction interval
Small sample
Testing
Prediction
Range of data

Keywords

  • engineering
  • statistics

Cite this

Quigley, J. L., & Walls, L. A. (2006). Prediction intervals for reliability growth models with small sample sizes. In Springer Handbook of Engineering Statistics (pp. 113-124). Heidelberg: Springer-Verlag.
Quigley, J.L. ; Walls, L.A. / Prediction intervals for reliability growth models with small sample sizes. Springer Handbook of Engineering Statistics. Heidelberg : Springer-Verlag, 2006. pp. 113-124
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Quigley, JL & Walls, LA 2006, Prediction intervals for reliability growth models with small sample sizes. in Springer Handbook of Engineering Statistics. Springer-Verlag, Heidelberg, pp. 113-124.

Prediction intervals for reliability growth models with small sample sizes. / Quigley, J.L.; Walls, L.A.

Springer Handbook of Engineering Statistics. Heidelberg : Springer-Verlag, 2006. p. 113-124.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Quigley JL, Walls LA. Prediction intervals for reliability growth models with small sample sizes. In Springer Handbook of Engineering Statistics. Heidelberg: Springer-Verlag. 2006. p. 113-124