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Activity Number:
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522
- Contributed Poster Presentations: Section on Physical and Engineering Sciences
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Type:
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Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #322448
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Title:
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Finite Population Agresti-Coull Based Binomial Confidence Intervals and Excel VBA Function
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Author(s):
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William Harper*
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Companies:
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Otterbein University
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Keywords:
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binomial ;
proportion ;
confidence interval ;
finite population ;
Agresti-Coull
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Abstract:
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Agresti-Coull (1998) developed an improvement over the classical central limit theorem based confidence interval for a single binomial proportion; however, it is based on the assumption of an infinite population. The 2nd edition (2013) of API-1163 used in the oil/gas industry to compare in-line inspection tools to excavated corrosion anomalies introduced the Agresti-Coull approach to the pipeline industry. While the exact Clopper-Pearson (1934) binomial confidence interval approach avoids many of the issues of any central limit theorem approximations (including Agresti-Coull) its coverage often exceeds the stated confidence level. For example 95% confidence intervals may average (say) 96% coverage while the Agresti-Coull intervals more closely track to the desired coverage level. In the oil/gas industry small sample sizes are not uncommon and the calling population is often finite. This paper expands the Agresti-Coull method to accommodate finite populations. In addition an Excel VBA function has been developed that creates Agresti-Coull single population binomial confidence intervals as well as expansions beyond the one-tailed upper bound used in API 1163.
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Authors who are presenting talks have a * after their name.
