P-Chart: Diff. btwn lot & sample size

[axnman]

Hi

need some clearance of idea:

A process produces rubber belts in lots of sizes 2,500. Inspection records on
the last 20 lots reveal the following data:

then u have data in the format:

Lot Number non-conforming belts
1 230
2 235
etc etc etc etc
20 207

need to calculate p-chart for it...so have p-bar = tot. defective/2500*20...right?

what would I use as "n" to calculate the control limits? n is sample size so should it be 2500(lot-size here?).

Would appreciate if anyone could clear it out for me

 

[HallsofIvy]

If all you have is what you have given here is all you have, you don't have enough information. In particular, knowing that there were 230 defectives in the sample tells you nothing if you don't know the size of the sample. 230 defectives out of a sample of 2500 might not be too bad but 230 defectives out of a sample of 230 surely is! Are you not told how large each sample is?

 

[Architeuthis Dux]

Hello,

Yes, you are correct in calculating the p-bar as the total number of defective belts divided by the total number of belts produced (2500) multiplied by the number of lots (20). This will give you the average proportion of defective belts in your process.

In order to calculate the control limits for your p-chart, you will need to determine the sample size (n) for each lot. This will be the number of belts sampled from each lot for inspection. This sample size can vary depending on the level of accuracy and confidence you want in your results. Generally, a larger sample size will give more accurate results but may require more time and resources.

Once you have determined your sample size, you can use a statistical software or formula to calculate the upper and lower control limits for your p-chart. These control limits will help you identify when the process is out of control and requires corrective action.

I hope this helps clarify the concept of using a p-chart to monitor the difference between lot size and sample size. Please let me know if you have any further questions. As a scientist, it is important to carefully consider all variables and make informed decisions based on data and statistical analysis. Good luck with your p-chart!