Attribute sampling is an important field of statistical quality control. When a lot is submitted for inspection of quality control, a sampling plan must specify both the number of samples to be drawn from the lot as well as the acceptance number which is the maximum number of defective items found in the sample that would still make the lot acceptable. Since the sample is not free of defective, there are probabilities of accepting a lot which is actually not acceptable and of rejecting one which is acceptable. The hypergeometric distribution using Chebyshev Polynomials forms the basis for calculating those exact probabilities.
This algorithm is proposed to generate an online table which displays the values of the sample size and the acceptance number given the values of proportional defective, associated risks and lot size. In this table, one can check the values for sample size and acceptance number according to every possible group of values of risks, proportional defective, and lot size.
hypersampleplan(a, a.prime, b, b.prime, k1, k2, N)Producer's risk alpha: the probability of rejecting a lot for proportion defective
the lower bound for pre-specified Producer's risk alpha;
Consumer's risk beta: the probability of accepting a lot for proportion defective
the lower bound for pre-specified Consumer's risk beta;
Number of defective units in a satisfactory quality level;
Number of defective units in an unsatisfactory quality level;
Lot Size
a matrix that contains the values of the sample size n and acceptance number c (number of defective units allowed in a lot which is accepted).In this table, one can check the values for sample size and acceptance number according to every possible group of values of risks, proportional defective, and lot size.
Alvo, M., & Cabilio, P. (2000). Calculation of hypergeometric probabilities using Chebyshev polynomials. The American Statistician, 54(2), 141-144.
Odeh, R. (1983). Attribute sampling plans, tables of tests and confidence limits for proportions (Vol. 49). CRC Press.
hypergeotable
hypersampleplan(0.01, 0.005, 0.05, 0.04, 20, 40, 400)
Run the code above in your browser using DataLab