I'm stuck with this problem on quality control and p-charts.
There is a given process, monitored periodically taking samples of 100 items each and counting the number that are defective. In the last 50 samples we found a total of 622 defective items.
I'm OK for the 3-sigma limits of the p-chart and in particular: $$\bar{p} = \frac{622}{50*100} = 0.1244$$ $$\text{UCL} = \bar{p} + 3\sqrt{\frac{\bar{p}(1-\bar{p})}{n}} = 0.1244+ 3\sqrt{\frac{0.1244 (1-0.1244)}{100}} = 0.2234$$ $$\text{LCL} = \bar{p} - 3\sqrt{\frac{\bar{p}(1-\bar{p})}{n}} = 0.1244+ 3\sqrt{\frac{0.1244 (1-0.1244)}{100}} = 0.0254$$ where $\bar{p}$ represents the estimated proportion of defects from the process, used to calculate the control limits.
Now, how am I supposed to find the average number of samples before having a point falling outside the control limits (signaling an of out-of-control), given an actual shift of the estimated proportion of defects from $\bar{p}$ to $p' = \bar{p} + 0.06 = 0.1844$ due, for example, to something new that is now influencing the process?
I think this has to do with the average run length $\text{ARL}_1 = \frac{1}{1-\beta}$, but I'm not quite sure on the steps leading to its value.