acc.samp {tolerance} | R Documentation |
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.
acc.samp(n, N, alpha = 0.05, P = 0.99, AQL)
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. |
acc.samp
returns a data frame with items:
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 . |
RQL |
The rejectable quality level. This is the proportion of individual items in a sample one is willing
to tolerate missing (i.e., this is 1-P ). |
confidence |
The confidence level 1-alpha . |
AQL |
The acceptable quality level. 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. |
sample.size |
The sample size drawn as specified by n . |
prod.risk |
The producer's risk. 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). |
cons.risk |
The consumer's risk. 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 (e.g., most of the missing
items happend to not be selected for the audit). 1-cons.risk gives the actual confidence level of this
sampling plan. If it is lower than the confidence level desired (i.e., because too small a sample size was
specified), then a warning message will be displayed. |
Montgomery, D. C. (2005), Introduction to Statistical Quality Control, Fifth Edition, John Wiley & Sons, Inc.
## 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 = 0.01)