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MSA 101

published on October 21, 2013

We’ve talked about accuracy, repeatability and reproducibility in your MSA’s but now we need to talk about data integrity.

Data Integrity

Numbers shouldn’t lie, but when they do it is usually because somewhere along the line the integrity of the data didn’t hold up.

Integrity

Before you begin your analysis there are two questions you should ask yourself:

  • Does my data have known reference points?
  • Does the data match control documents? If you’re looking at product returns, does the data match the information on your financial documents?

Accuracy and Precision

The next thing to think about is accuracy and precision. When you are evaluating the accuracy of your data, what you are looking for is how close the average is to the anticipated value. Your precision will tell you how much variation occurs in you data. Think about it in terms of playing pool. Your accuracy tells you how close you were to making the shot and your precision shows you how far apart the balls were from the pocket.

Bias

The third thing to look at is any bias your data might have. Formally the definition of bias is the deviation of what was measured from the actual value. What that means is how far off your measurement is from the actual number. The goal is to reduce bias as much as possible, I say reduce because you will never be able to eliminate it.  You will need to decide what acceptable bias limits are. If you have a worker who is consistently late and you’re measuring organizational tardiness, you know your bias is going be about 10 minutes.

Stability

Next you can move on to stability. Stability is defined as your error rate. The less errors, the more stable the process. All stability does is tell you when accuracy or bias changes in your process.  What you should be looking for it to do is serve as an alarm, letting you know that something has changed. This alerts you to areas in your process that are no longer stable.

Linearity

Last but not least, you have linearity. What this tells you is if your bias is consistent. If something happens once, it’s an outlier. It’s not consistent which means you don’t want to hinge a change or a new process on something that may or may not happen again.

 

MSA is a big subject and we are far from done with it. Next week we will continue to talk about MSA Windows in Minitab and how to interpret them. In the meantime if you have any questions give us a call and let us help!

published on October 21, 2013

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