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Attribute Gage R&R
An Overview
Presented to ASQ Section 1302August 18, 2011
by Jon Ridgway
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Overview
What is an Attribute Gage R&R?
Why is it worth the work?
What are the caveats?
How do I perform it? How do I understand the results?
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My Attribute Gage R&R Experience
First Data
Credit Card mailings (packages)
Airlite Plastics:
o r nt ua ty Print Quality, Cup & Lid Decoration
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WHY conduct a Gage R&R????
Some processes require subjective decision
making: Inspection
Validation
Subjectivity creates the potential for variation Measurement System variation impacts process
capability:
Type I Errors
Type II Errors
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So, Why Conduct a Gage R&R?
To understand:
How likely Appraiser will agree with himself / herself: WITHIN / Repeatability
How likely all Appraisers will agree with each other:
BETWEEN / Reproducibility Understanding R&R allows you to:
Predict probability (%) of agreement / disagreement
Implement training to improve that probability Reduce Type I and Type II Errors = $$$
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MSA
The Foundation of everything in Quality is
measurement
Measure for two primary reasons:
o ma e a ec s on As the basis for process improvement
Can we trust our measurement system to
give us reliable data? CONFIDENCE
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Ultimate purpose of the AttributeAgreement Analysis
To determine if your measurement system
can distinguish between a good & bad part
Accuracy & Precision:
ccuracy: Absence of bias, or agreeing with the standard.
Precision:
Ability of different Appraisers to reach the sameconclusion several times.
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Accurate, But Not Precise
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Precise, But Not Accurate
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Which is Easier to Remedy?
Accurate, but not Precise Precise, but not Accurate
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Gage R&R Review
Measurement System Analysis (MSA)
1st R: Repeatability
2nd R: Reproducibility
a a n enera : Continuous / Variables
Attribute / Discrete
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Attribute vs. Continuous
Attribute Data:
Categorical, named only, arbitrary scalesAlso known as Discrete Data
Continuous Data:
Allows for infinitely finer sub-divisions
Also known as Variables Data
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Basic Data Types
Nominal: Literally, name
Represents categories Ordinal:
Orderedor ranked data
Interval: Measured / scaled data: Each position equidistant
0 can be relevant (temperature)
Ratio: Numbers compared as multiples of one another
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Hierarchy of Data TypesNominal Ordinal Interval Ratio
Classified Data Quantified Data
DISCRETE / ATTRIBUTE CONTINUOUS / VARIABLE
Non-parametric Parametric
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2 Main Attribute Gage R&R Types1) Binary / Nominal
GO / NO GO Data are Categorical and mutually exclusive
Kappa statistic is relevant
2) Ordinal Rank, not categorical
Data are not mutually exclusive
Kendalls statistic more relevant than Kappa
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Kappa Statistic Proportion of agreement between
evaluators after chance agreement hasbeen removed:
Kappa = P observed P chance/ P chance
Expressed as a number: From 0 (expected by chance)
Up to +1 (complete agreement)
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Kendalls Statistics
Two different Kendalls for different tests:
Kendalls Coefficient of Concordance: Rankings without a known Standard
Rankings with a known Standard
Expressed as 0 (weaker agreement) to +1(stronger agreement)
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Kappa & Kendalls Summary
Kappa:
Nominal / Binary Only
Match or No Match
en a s oe c en o oncor ance:Ordinal but not using a known Standard
Kendalls Correlation Coefficient:
Ordinal and using a known Standard
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Attribute Gage R&R Considerations
Study Purpose
Destructiveness
Precision vs. Time
Binomial / Nominal vs. Ordinal
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MSA Factors Impacting Variation
Gage
Appraiser
Method
Part Environment
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Controlling MSA Factors
Ideally:
1. Use the same Assessment Method
2. Require all Appraisers to assess the same
3. Conduct the study under the normalassessment conditions
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Controlling MSA Factors, Cont.
1) Appraisers: Select from group that
normally appraises the part.2) Number of parts should cover the entire
.
3) More than one appraisal per Appraisershould be done.
4) The presentation of the samples withinthe Trial should be randomized.
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Nominal / Binary Study Two Appraisers
50 Parts
2 Trials
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Output: Within
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Output: Within vs. Standard
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100
95
95.0% C IPercent
100
95
95.0% C IPercent
Date of study:
Reported by:Name of product:
Misc:
Assessment Agreement
Within Appraisers Appraiser vs Standard
FredLee
90
85
80
Appraiser
Percen
t
FredLee
90
85
80
Appraiser
Percen
t
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Output: Between
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Output: Between vs. Standard
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Ordinal Case Study: Print Quality
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What did we want to know?
Do all Appraisers of Print Quality:
Agree consistently with Themselves?
Agree consistently with Each Other?
,
Accept
Accept but Adjust
Reject
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How was it Done?
10 samples, Good & Bad
Random Order, Same for All
2 Trials per person
All people in the study Environment
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Ensure Gage R&R Consistency
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Spanish Version
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Vietnamese Version
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Trial Order
My Checklist
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ResultsSample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard
1 1 1 2 2 2 2 2
2 2 2 2 2 3 3 3
3 1 1 2 2 2 2 2
5 2 1 3 3 2 3 2
6 1 1 2 2 2 2 2
7 2 1 3 3 3 3 3
8 2 2 2 2 2 2 3
9 1 1 2 2 3 3 1
10 1 1 1 1 2 2 1
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False Alarms & Misses Assess Fail when Standard = Pass:
False Alarm Type I Error
ssess ass w en an ar = a :
Miss
Type II Error
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ResultsSample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard1 1 1 2 2 2 2 2
2 2 2 2 2 3 3 33 1 1 2 2 2 2 2
4 2 2 3 3 2 2 3
False
Alarms
5 2 1 3 3 2 3 26 1 1 2 2 2 2 2
7 2 1 3 3 3 3 3
8 2 2 2 2 2 2 39 1 1 2 2 3 3 1
10 1 1 1 1 2 2 1
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Minitab 15 Four Results:
1. Within2. Within vs. Standard
.
4. Between vs. Standard
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Check Here
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100
80
95.0% C I
Percent
100
80
95.0% C I
Percent
Date of study :
Reported by:
Name of product:Misc:
Assessment Agreement
Within Appraisers Appraiser vs Standard
QA-4QA-2QA-1
60
40
20
0
Appraiser
Perce
nt
QA-4QA-2QA-1
60
40
20
0
Appraiser
Perce
nt
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Two Big Lessons You cant trust your data until it is proven
to be trustworthy. A single, one-time Gage R&R study is
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Questions? Thank You!
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