Logit and Probit Models for O-Ring Failure Data
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Logit and probit models are common binomial models for success or failure probabilities. Following the 1986 Challenger shuttle disaster due to a failed O-ring, the impact of air temperature on the the probability of at least one primary O-ring failure was analyzed from data from previous flights using a logit model. The analysis found that the probability of failure increased dramatically as temperature decreased.
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Contributed by: Darren Glosemeyer (March 2011)
Open content licensed under CC BY-NC-SA
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The logit models have the form 
. Probit models have the form 
. The term 
is called the linear predictor and is linear in the parameters 
. The linear predictor is 
. The quadratic linear predictor is 
.
The data is based on table 5.10 from A. Agresti, An Introduction to Categorical Data Analysis, New York: Wiley, 1996.
The original source of the data is table 1 from S. R. Dalal, E. B. Fowlkes, and B. Hoadley, "Risk Analysis of Space Shuttle: Pre-Challenger Prediction of Failure," Journal of the American Statistical Association, 84(408), 1989 pp. 945–957, in which logistic regression models were used.
Permanent Citation
"Logit and Probit Models for O-Ring Failure Data"
http://demonstrations.wolfram.com/LogitAndProbitModelsForORingFailureData/
Wolfram Demonstrations Project
Published: March 7 2011
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