The OC curve is developed by evaluating equation (15.2) for various values of p. Table 15.2
displays the calculated value of several points on the curve.
The OC curve shows the discriminatory power of the sampling plan. For example, in
the sampling plan n = 89, c = 2, if the lots are 2% defective, the probability of acceptance is
approximately 0.74. This means that if 100 lots from a process that manufactures 2% defective
product are submitted to this sampling plan, we will expect to accept 74 of the lots and
reject 26 of them.
Effect of n and c on OC Curves. A sampling plan that discriminated perfectly
between good and bad lots would have an OC curve that looks like Fig. 15.3. The OC curve
runs horizontally at a probability of acceptance Pa = 1.00 until a level of lot quality that is
considered “bad” is reached, at which point the curve drops vertically to a probability of
acceptance Pa = 0.00, and then the curve runs horizontally again for all lot fraction defectives
The OC curve is developed by evaluating equation (15.2) for various values of p. Table 15.2displays the calculated value of several points on the curve.The OC curve shows the discriminatory power of the sampling plan. For example, inthe sampling plan n = 89, c = 2, if the lots are 2% defective, the probability of acceptance isapproximately 0.74. This means that if 100 lots from a process that manufactures 2% defectiveproduct are submitted to this sampling plan, we will expect to accept 74 of the lots andreject 26 of them.Effect of n and c on OC Curves. A sampling plan that discriminated perfectlybetween good and bad lots would have an OC curve that looks like Fig. 15.3. The OC curveruns horizontally at a probability of acceptance Pa = 1.00 until a level of lot quality that isconsidered “bad” is reached, at which point the curve drops vertically to a probability ofacceptance Pa = 0.00, and then the curve runs horizontally again for all lot fraction defectives
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