att-gage

Attribute Gage R & R Effectiveness
Instructions:
1) The following spreadsheet is used to calculate an Attribute GR&R Effectiveness, in which up to
100 samples can be evaluated, using 2 or 3 operators.

2) In the Data Entry worksheet fill in the appropriate information in the Scoring Report section and
enter the type of Attributes you are evaluating in the Attribute Legend section. YOU MUST ENTER
THE INFORMATION IN THE ATTRIBUTE LEGEND SECTION OR THE SPREADSHEET
WILL NOT WORK. The attributes can be either alpha or numeric, e.g. Yes, No; pass, fail;
go, stop; or 1, 2. You must be consistent throughout the form and spell properly.

3) If you or an expert has selected samples to be evaluated and you know what attributes these
samples are (Good vs Bad), enter this information in the STANDARD column. This will enable you to
determine how well each operator can evaluate a set of samples against a known standard. You do not
need to enter information in this column for the spreadsheet to work although you will
not be able to assess the operators against known standards.

4) You do not have to specify how many operators or the # of samples that you will be evaluating
during the test. Simply enter the data into the spreadsheet under the specific operator. Remember
the attributes must be spelled properly or the spreadsheet will not analyze the data correctly.

5)
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8)

To print a copy of the report click on the Print Report icon.
To delete the data in the spreadsheet, click on the Delete Data icon.
To delete all and begin a new test, click on the Delete Data icon
To see a Demo of the Attribute GR&R Effectiveness spreadsheet, click on the Demo icon.
Move around the spread sheet to see the data. When you are finished click the Delete Data icon
to delete all data to begin entering your own data.

The 95% UCL and 95% LCL represent the 95% upper and lower confidence limits on the
binomial distribution. The Calculated Score is the basic computation reported on the report
page for % Appraiser and % Score vs Attribute. The 95% confidence interval represents
the range within which the true Calculated Score lies given the uncertainty associated with
limited sample sizes. As sample size increases (in this case, Total Inspected) the
confidence interval will get smaller and smaller which indicates more reliable estimates of
the true percentages. In the case of the Demo data, the true Calculated score for Operator 1
could be as low as 76.8% given that only 14 samples inspected, even though there was a 100%

Appraiser value calculated. Also, even though Operator 2 had a lower score, Operators 1 and 3
cannot be distinguished from Operator 2 because the calculated score of #2 (78.6%) lies within
the confidence limits for Operators 1 and 3.
With a worksheet limitation of 100 samples, at best a lower 95% limit of 96.4% can be calculated.
Thus, we would have to say that an inspector could be as bad as 96% efficient, even though he/she
missed no calls.
100
100
100.0%
100.0%
96.4%

< Try out different combinations of number of samples and number of matches
< to see the effects of sample size. In this case, a sample size of 30 with
< one non-match will yield a 17% confidence interval. In order to get reasonable
< reliability in estimates of efficiency, large sample sizes will be required.

was a 100%

ors 1 and 3

) lies within

n be calculated.

n though he/she

nd number of matches
ple size of 30 with
n order to get reasonable
will be required.

Attribute Gage R & R Effectiveness

(Must Enter Information)

DATE:
NAME:

1

PRODUCT:

2

BUSINESS:

Optional: Enter Operator Name or use Default
Operator #1

Operator #2

1
2
3
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32

Standard

Try #1

Try #2

Try #1

Try #2

Y/N

Y/N

Agree

Agree

Operator #3

Known Population
Sample
#

All Operators agree with
standard

Attribute Legend5

All operators agree witin
and between each other

SCORING REPORT

Try #1

Try #2

33
34
35
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100
% APPRAISER SCORE(1) ->

#DIV/0!

#DIV/0!

0%

% SCORE VS. ATTRIBUTE(2) ->

Known

Known

Known

SCREEN % EFFECTIVE SCORE(3) ->

0.00%

SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE
Note:
(1) Operator agrees with him/herself on both trials
(2) Operator agrees on both trials with the known standard
(3) All operators agreed within and between themselves
(4) All operators agreed within and between themselves AND agreed with the known standard
(5) Enter Pass/Fail, Good/Bad, Accept/Reject or other labels which indicate status of inspection

(4)

->

0.00%

Statistical Report - Attribute Gage R&R Study
DATE:

30-Dec-1899

NAME:

PRODUCT: 0

BUSINESS: 0

% Appraiser to Self1
Operator Operator Operator
#1
#2
#3
0
0
0
0
0
0

Source
Total Inspected
# Matched
False Negative (operator biased toward rejection) Std = Pass
False Positive (operator biased toward acceptance) Std = Fail
Mixed (Operator accepts and rejects the same part)
95% UCL
#DIV/0!
#DIV/0!
#DIV/0!
Calculated Score
#DIV/0!
#DIV/0!
#DIV/0!
95% LCL
#DIV/0!
#DIV/0!
#DIV/0!
Screen % Effective
Score3
100
0
3.6%
0.0%
0.0%

Total Inspected
# in Agreement
95% UCL
Calculated Score
95% LCL

0

% Appraiser Vs Standard2
Operator Operator Operator
#1
#2
#3
0
0
0
0
0
0

0
0
0

0
0
0

0
0
0

#DIV/0!
#DIV/0!
#DIV/0!

#DIV/0!
#DIV/0!
#DIV/0!

#DIV/0!
#DIV/0!
#DIV/0!

Screen % Effective
Score vs Standard4
100
0
3.6%
0.0%
0.0%

% Apprrais er to Self

% Apprais er Vs Standard
95% LCL

100.0%

100.0%

90.0%

90.0%

80.0%

80.0%

70.0%

70.0%

60.0%

60.0%

% Efficie ncy

% Efficiency

95% LCL

50.0%
40.0%

50.0%
40.0%

30.0%

30.0%

20.0%

20.0%

10.0%

10.0%

0.0%

0.0%
1

2

3

1

Notes
1) Operator agrees with him/herself on both trials
2) Operator agrees on both trials with the known standard
3) All operators agreed within and between themselves
4) All operators agreed within & between themselves AND agreed with the known standard

2

3