OC curve

OC curve

[¦ō′sē ‚kərv]
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In Section 4, we will compare the efficiency of the proposed RGS plan with the single sampling plan in terms of OC curve and the average sample number (ASN).
It can be seen that the OC curve becomes more like the idealized OC curve shape (the slope is getting larger) as the sample size increases.
In this section, we will use these two criteria, the OC curves and the sample size required for inspection, to demonstrate the advantages of the proposed RGS plan over the single plan based on the index [C.
18] analyzed the properties of OC curve of the acceptance sampling plans based on gamma-Poisson distribution.
In this paper, we use two points on the OC curve approach to design the RGS plan.
One of the desirable properties of an OC curve is that the decrease of the probability of acceptance should be slower for lesser values of 'p' (good quality level) and steeper for larger values of 'p' (bad quality level), which provides a better overall discrimination.
In general however, it is difficult to compute the OC curve directly.
The corresponding points on the OC curve are (0, 1), ([[Mu].
Each OC curve corresponds to the estimated probability of correct classification and is determined by subtracting the joint probability estimated above from the number one.
The OC curves are based on the estimated joint probability of the following two independent events that determine whether a patient will be incorrectly classified: (a) the laboratory obtains a measured value within the range for high risk when the true homeostatic set point is in the range for desirable risk, or the laboratory obtains a measured value within the range for desirable risk when the true homeostatic set point is in the range for high risk; and (b) the QC samples) measured during the analytical run in which the patient specimen is analyzed are within acceptable limits.
A simple subjective way to arrive at the "optimum" risks is for an agency and its contractors to examine together the OC curves for a given acceptance plan.
The process involves economic decision theory and, like the subjective approach, requires an understanding of OC curves (probabilities) and economics (cost of consequences).