Negative binomial control limits for count data with extra‐Poisson variation
David Hoffman
Abstract
David Hoffman
Abstract
Abstract Traditional techniques for calculating control limits for processes with discrete responses are based on the Poisson distribution. However, for many processes, the assumption of a Poisson distribution is violated. In such cases, use of traditional Poisson control limits may result in an inflated risk of Type I error. The negative binomial distribution is a natural extension of the Poisson distribution and allows for over‐dispersion relative to the Poisson distribution. A simple approach to calculating exact and approximate control limits for count data based on the negative binomial distribution is described. The approach is illustrated by application to water bacteria count data taken from a water purification system. Copyright © 2003 John Wiley & Sons, Ltd.
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Abstract Traditional techniques for calculating control limits for processes with discrete responses are based on the Poisson distribution. However, for many processes, the assumption of a Poisson distribution is violated. In such cases, use of traditional Poisson control limits may result in an inflated risk of Type I error. The negative binomial distribution is a natural extension of the Poisson distribution and allows for over‐dispersion relative to the Poisson distribution. A simple approach to calculating exact and approximate control limits for count data based on the negative binomial distribution is described. The approach is illustrated by application to water bacteria count data taken from a water purification system. Copyright © 2003 John Wiley & Sons, Ltd.
Key concepts: Count data, Negative binomial distribution, Poisson distribution, Compound Poisson distribution, Poisson binomial distribution, Binomial distribution, Mathematics, Quasi-likelihood