# Linearity & Bias Acceptable Percentage Guideline - Digimatic micrometer

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#### philiplim

Hi,

I'm doing a linearity study on my digimatic micrometer. I'm using Minitab 13 to analyse my results. May I know what is the acceptable percentage for linearity & bias and where can I find these guidelines?

Thanks.

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#### Miner

##### Forum Moderator
Staff member
I have not seen a published set of guidelines for %Linearity. Have any others?

The AIAG MSA manual uses a different approach to assess suitability for Linearity. This method calculates the linearity regression line and applies "control" limits about the regression line based on the repeatability variation seen during the study.

If the Zero Bias/Zero Linearity line falls 100% within these "control" limits, the gage is considered acceptable for Linearity.

#### Atul Khandekar

##### Quite Involved in Discussions
There used to be a calculation for a %Linearity/ %Bais number in MSA 2nd Edition. But I am not aware of any acceptance criteria defined on the basis of this percentage.

In the third edition, the acceptance for both Linearity and Bias is based on t-tests and confidence interval calculations. %Linearity concept is dropped.

According to AIAG's FAQs: ------------------------
Q:Why did we drop the %Bias and %Linearity?
A:The reason we dropped the indices is that (1) there is no "correct" way to analyze them and (2) we want to focus on the understanding of the measurement system variability and sources of variation rather than on "acceptable" indices.

We went with the focus that the bias and linearity should be the statistical equivalent of zero -- consequently the confidence bounds and the test of hypothesis. If the bounds are large (i.e. the natural measurement system variability is large) then the bias can be statistically zero even though it may not be "emotionally" zero (i.e. a large percent in the old terms). However, because the variability is large, the system is unacceptable due to the other parameter evaluations and, furthermore, adjusting the bias (using this variation) can cause the bias to become worse even though the calculated index becomes better -- ala Deming's funnel experiments.

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