accsamp: Acceptance Sampling
In tolerance: Statistical Tolerance Intervals and Regions

Description Usage Arguments Value References See Also Examples

Provides an upper bound on the number of acceptable rejects or nonconformities in a process. This is similar to a 1-sided upper tolerance bound for a hypergeometric random variable.

1	acc.samp(n, N, alpha = 0.05, P = 0.99, AQL = 0.01, RQL = 0.02)

`n`	The sample size to be drawn from the inventory.
`N`	The total inventory (or lot) size.
`alpha`	`1-alpha` is the confidence level for bounding the probability of accepting the inventory.
`P`	The proportion of items in the inventory which are to be accountable.
`AQL`	The acceptable quality level, which is the largest proportion of defects in a process considered acceptable. Note that `0 < AQL < 1`.
`RQL`	The rejectable quality level, which is the largest proportion of defects in an independent lot that one is willing to tolerate. Note that `AQL < RQL < 1`.

acc.samp returns a matrix with the following quantities:

`acceptance.limit`	The number of items in the sample which may be unaccountable, yet still be able to attain the desired confidence level `1-alpha`.
`lot.size`	The total inventory (or lot) size `N`.
`confidence`	The confidence level `1-alpha`.
`P`	The proportion of accountable items specified by the user.
`AQL`	The acceptable quality level as specified by the user. If the sampling were to be repeated numerous times as a process, then this quantity specifies the proportion of missing items considered acceptable from the process as a whole. Conditioning on the calculated value for `acceptance.limit`, the `AQL` is used to estimate the producer's risk (see `prod.risk` below).
`RQL`	The rejectable quality level as specified by the user. This is the proportion of individual items in a sample one is willing to tolerate missing. Conditioning on the calculated value for `acceptance.limit`, the `RQL` is used to estimate the consumer's risk (see `cons.risk` below).
`sample.size`	The sample size drawn as specified by `n`.
`prod.risk`	The producer's risk at the specified `AQL`. This is the probability of rejecting an audit of a good inventory (also called the Type I error). A good inventory can be rejected if an unfortunate random sample is selected (e.g., most of the missing items happened to be selected for the audit). `1-prod.risk` gives the confidence level of this sampling plan for the specified `AQL` and `RQL`. If it is lower than the confidence level desired (e.g., because the `AQL` is too high), then a warning message will be displayed.
`cons.risk`	The consumer's risk at the specified `RQL`. This is the probability of accepting an audit of a bad inventory (also called the Type II error). A bad inventory can be accepted if a fortunate random sample is selected (e.g., most of the missing items happened to not be selected for the audit).

Montgomery, D. C. (2005), Introduction to Statistical Quality Control, Fifth Edition, John Wiley & Sons, Inc.

Hypergeometric

 
## A 90%/90% acceptance sampling plan for a sample of 450 
## drawn from a lot size of 960.

acc.samp(n = 450, N = 960, alpha = 0.10, P = 0.90, AQL = 0.07,
         RQL = 0.10)