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Title
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Statistical Methods for Reliability Data
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Date / Time / Room
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Sponsor
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Type
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08/11/2002
8:15 AM -
4:15 PM
Room: H-Harlem Suite
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ASA, Section on Physical & Engineering Sciences*, Section on Quality & Productivity*
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Other
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Organizer:
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n/a
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Chair:
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n/a
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CE Presenter
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Luis A. Escobar - Louisiana State University
Luis A. Escobar - Louisiana State University
Luis A. Escobar - Louisiana State University
William Q. Meeker - Iowa State University
William Q. Meeker - Iowa State University
William Q. Meeker - Iowa State University
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Description
Reliability assurance processes in manufacturing industries require data-driven information for making product-design decisions. Life tests, accelerated life tests, and accelerated degradation tests are commonplace. Data on products in the field provide another important source of useful reliability information. Reliability studies typically require nonstandard methods of analysis.
The course makes modern methods for analyzing failure-time and degradation data available to a wide audience of practitioners. The course will describe and illustrate the use of a mix of proven traditional techniques, enhanced and brought up to date with modern computer-based methodology. Topics to be covered include censored data, nonparametric estimation, probability plotting, maximum likelihood estimation, likelihood-based confidence intervals, acceleration models, accelerated life testing, and accelerated degradation testing. The general concepts and methods in this course also have applications in medicine, life sciences, sociology, economics, and other sciences. Most of the examples in the course will come from applications of products reliability, but some biological examples will also be presented to illustrate the breadth of application.
This course will focus primarily on applications, data, concepts, methods and interpretation. There will be little or no theory presented in the course and results of complicated computations will be illustrated graphically. As such, the required technical background for the course is minimal. The material in this course will be of interest and accessible to individuals ranging from engineers having had only one or two statistics courses in their education/training through individuals with advanced degrees in statistics.
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