Best Paper- A Practitioner’s Guide to Analyzing Reliability Experiments with Random Blocks and Subsampling

Jennifer Kensler
Jennifer Kensler is a Statistical Consultant at Shell Global Solutions in Houston, TX. She has a B.A. in Mathematics from Grinnell College, an M.A. in Mathematics from the University of Kansas, and an M.S. and a Ph.D. in Statistics from Virginia Tech. Her research interests include design of experiments and reliability.


Reliability experiments provide important information regarding the life of a product, including how various factors affect product life. Current analyses of reliability data usually assume a completely randomized design. However, reliability experiments frequently contain subsampling, which represents a restriction on randomization. A typical experiment involves applying treatments to test stands, with several items placed on each test stand. In addition, raw materials used in experiments are often produced in batches, leading to a design involving blocks. This presentation proposes a method using Weibull regression for analyzing reliability experiments with random blocks and subsampling. An illustration of the method is provided.

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