Attribute Gage R & R EffectivenessInstructions:

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)

THE INFORMATION IN THE ATTRIBUTE LEGEND SECTION OR THE SPREADSHEET

3) If you or an expert has selected samples to be evaluated and you know what attributes these

need to enter information in this column for the spreadsheet to work although you willnot 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

the attributes must be spelled properly or the spreadsheet will not analyze the data correctly.5)6)7)8)

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 thebinomial distribution. The Calculated Score is the basic computation reported on the reportpage for % Appraiser and % Score vs Attribute. The 95% confidence interval representsthe range within which the true Calculated Score lies given the uncertainty associated withlimited sample sizes. As sample size increases (in this case, Total Inspected) the confidence interval will get smaller and smaller which indicates more reliable estimates ofthe true percentages. In the case of the Demo data, the true Calculated score for Operator 1could 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 3cannot be distinguished from Operator 2 because the calculated score of #2 (78.6%) lies withinthe 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.

In the Data Entry worksheet fill in the appropriate information in the Scoring Report section andenter the type of Attributes you are evaluating in the Attribute Legend section.

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.

samples are, enter this information in the Attribute sample column. This will enable you to determine how well each operator can evaluate a set of samples against a known standard.

during the test. Simply enter the data into the spreadsheet under the specific operator.

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 All iconTo see a Demo of the Attribute GR&R Effectiveness spreadsheet, click on the Move around the spread sheet to see the data. When you are finished click the

Thus, we would have to say that an inspector could be as bad as 96% efficient, even though he/shemissed no calls.

Sample Size 30 < Try out different combinations of number of samples and number of matches# Matches 30 < to see the effects of sample size. In this case, a sample size of 30 with 95% UCL 100.0% < one non-match will yield a 17% confidence interval. In order to get reasonableCalculated Score 100.0% < reliability in estimates of efficiency, large sample sizes will be required.95% LCL 88.4%

The following spreadsheet is used to calculate an Attribute GR&R Effectiveness, in which up to

THE INFORMATION IN THE ATTRIBUTE LEGEND SECTION OR THE SPREADSHEET

If you or an expert has selected samples to be evaluated and you know what attributes these

need to enter information in this column for the spreadsheet to work although you will

You do not have to specify how many operators or the # of samples that you will be evaluating

the attributes must be spelled properly or the spreadsheet will not analyze the data correctly.

The 95% UCL and 95% LCL represent the 95% upper and lower confidence limits on thebinomial distribution. The Calculated Score is the basic computation reported on the reportpage for % Appraiser and % Score vs Attribute. The 95% confidence interval representsthe range within which the true Calculated Score lies given the uncertainty associated withlimited sample sizes. As sample size increases (in this case, Total Inspected) the confidence interval will get smaller and smaller which indicates more reliable estimates ofthe true percentages. In the case of the Demo data, the true Calculated score for Operator 1could 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 3cannot be distinguished from Operator 2 because the calculated score of #2 (78.6%) lies within

With a worksheet limitation of 100 samples, at best a lower 95% limit of 96.4% can be calculated.

worksheet fill in the appropriate information in the Scoring Report section andenter the type of Attributes you are evaluating in the Attribute Legend section. YOU MUST ENTER

The attributes can be either alpha or numeric, e.g. Yes, No; pass, fail;You must be consistent throughout the form and spell properly.

sample column. This will enable you to determine how well each operator can evaluate a set of samples against a known standard. You do not

during the test. Simply enter the data into the spreadsheet under the specific operator. Remember

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 All icon

Thus, we would have to say that an inspector could be as bad as 96% efficient, even though he/she

< 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.

Attribute Gage R & R Effectiveness

SCORING REPORT

12

Known Population Operator #1Sample # Attribute Try #1 Try #2

1 FMPG 11.21 Jan-002 OK OK OK3 LSD LSD LSD4 ILB ILB ILB5 KB KB KB6 OK FMPG FMPG7 LB LB LB8 OK OK OK9 LT LT LT

10 OK LBD LBD11 FMP FMP FMP12 BB BB BB13 DF BT BT14 OK OK OK15 FMST FMST FMST16 LBD LBD OK17 OK LBD LBD18 OK OK OK19 CT CT CT20 AI AI AI21 OK OK OK22 NB DM DM23 DFT FM FM24 UC TBW TBW25 OK OK OK26 CFC DM DM

Attribute Legend 5 (used in computations)

27 FC FC FC28 CTT CTT CTT29 OK DM DM30 FMLR FMLR FMLR31323334353637383940414243444546474849505152535455565758596061626364656667

6869707172737475767778798081828384858687888990919293949596979899100

96.67%63.33%

% APPRAISER SCORE(1) ->% SCORE VS. ATTRIBUTE(2) ->

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

Attribute Gage R & R Effectiveness

SCORING REPORTDATE: 10/06/2004NAME: Final Inspection

PRODUCT: Tyres All operators

BUSINESS: agree within and

between each

Other

Operator #2 Operator #3 Y/NTry #1 Try #2 Try #1 Try #2 AgreeLBD LBD LBD LBD YOK OK OK OK YLSD LSD LSD LSD YOK OK OK OK NKB KB DBL KB NOK OK OK OK NLB LB LB LB YOK OK TBW TBW NLT LT LT LT YLT OK LBD LBD N

FMP FMP FMP FMP YOK BB BB BB NDF DF DFT DFT NOK OK OK OK YLBD FMST FMC FMC NLBD LBD LBD LBD NOK LBD OK OK NOK OK OK OK YCT FMP CTL CTL NAI AI AI AI YOK OK OK OK YOK RB NB NB NDFT DFT DFT DFT NTBW TBW UC UC NOK OK OK OK YOK CFC CFC CFC N

FC FC FC FC YOK OK OK DM NTB OK TBW TBW N

FMLR FMLR FMLR FMLR Y

76.67% 96.67%60.00% 66.67%

43.33%SCREEN % EFFECTIVE SCORE(3) ->SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE (4) ->

Operator agrees on both trials with the known standardAll operators agreed within and between themselvesAll operators agreed within and between themselves AND agreed with the known standardEnter Pass/Fail, Good/Bad, Accept/Reject or other labels which indicate status of inspection

All Operators

agree with

standard

Y/NAgree

NYYNNNYNYNYNNYNNNYNYYNNNYN

YNNY

40.00%

Attribute Gage R & R Effectiveness

SCORING REPORTDATE: 10/06/2004

Attribute Legend NAME: Final Inspection1 0 PRODUCT: Tyres All operators

2 0 BUSINESS: 0 agree within and All Operatorsbetween each agree with

Other standard

Known Population Operator #1 Operator #2 Operator #3 Y/N Y/NSample # Attribute Try #1 Try #2 Try #1 Try #2 Try #1 Try #2 Agree Agree

1 FMPG 11.21 11.25 LBD LBD LBD LBD Y N2 OK OK OK OK OK OK OK Y Y3 LSD LSD LSD LSD LSD LSD LSD Y Y4 ILB ILB ILB OK OK OK OK N N5 KB KB KB KB KB DBL KB N N6 OK FMPG FMPG OK OK OK OK N N7 LB LB LB LB LB LB LB Y Y8 OK OK OK OK OK TBW TBW N N9 LT LT LT LT LT LT LT Y Y

10 OK LBD LBD LT OK LBD LBD N N11 FMP FMP FMP FMP FMP FMP FMP Y Y12 BB BB BB OK BB BB BB N N13 DF BT BT DF DF DFT DFT N N14 OK OK OK OK OK OK OK Y Y15 FMST FMST FMST LBD FMST FMC FMC N N16 LBD LBD OK LBD LBD LBD LBD N N17 OK LBD LBD OK LBD OK OK N N18 OK OK OK OK OK OK OK Y Y19 CT CT CT CT FMP CTL CTL N N20 AI AI AI AI AI AI AI Y Y21 OK OK OK OK OK OK OK Y Y22 NB DM DM OK RB NB NB N N23 DFT FM FM DFT DFT DFT DFT N N24 UC TBW TBW TBW TBW UC UC N N25 OK OK OK OK OK OK OK Y Y26 CFC DM DM OK CFC CFC CFC N N27 FC FC FC FC FC FC FC Y Y28 CTT CTT CTT OK OK OK DM N N29 OK DM DM TB OK TBW TBW N N30 FMLR FMLR FMLR FMLR FMLR FMLR FMLR Y Y

96.67% 76.67% 96.67%63.33% 60.00% 66.67%

43.33%40.00%

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

% APPRAISER SCORE(1) ->% SCORE VS. ATTRIBUTE(2) ->

SCREEN % EFFECTIVE SCORE(3) ->SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE(4) ->

Statistical Report - Attribute Gage R&R Study

DATE: 10/06/2004NAME: Final Inspection

PRODUCT: TyresBUSINESS: 12/30/1899

Source Operator #1 Operator #2 Operator #3 Operator #1 Operator #2 Operator #3Total Inspected 30 30 30 30 30 30# Matched 29 23 29 19 18 20

0 0 019 18 20

Mixed 1 7 195% UCL 99.9% 90.1% 99.9% 80.1% 77.3% 82.7%Calculated Score 96.7% 76.7% 96.7% 63.3% 60.0% 66.7%95% LCL 82.8% 57.7% 82.8% 43.9% 40.6% 47.2%

Total Inspected 30 30# in Agreement 13 1295% UCL 62.6% 59.4%Calculated Score 43.3% 40.0%95% LCL 25.5% 22.7%

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

% Appraiser1 %Score vs Attribute2

False Negative (operator biased toward rejection)

False Positive (operator biased toward acceptance)

Screen % Effective Score3 Screen % Effective Score vs Attribute4

Known Population Operator #1 Operator #2Sample # Attribute Try #1 Try #2 within known Try #1

1 2 2 2 1 0 22 2 1 1 1 1 13 2 1 1 1 1 14 2 1 1 1 1 25 2 1 1 1 1 16 2 2 2 1 0 17 2 1 1 1 1 18 2 1 1 1 1 19 2 1 1 1 1 1

10 2 2 2 1 0 211 2 1 1 1 1 112 2 1 1 1 1 213 2 2 2 1 0 114 2 1 1 1 1 115 2 1 1 1 1 216 2 1 2 0 0 117 2 2 2 1 0 118 2 1 1 1 1 119 2 1 1 1 1 120 2 1 1 1 1 121 2 1 1 1 1 122 2 2 2 1 0 223 2 2 2 1 0 124 2 2 2 1 0 225 2 1 1 1 1 126 2 2 2 1 0 227 2 1 1 1 1 128 2 1 1 1 1 229 2 2 2 1 0 230 2 1 1 1 1 13132333435363738394041424344

454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798

99100

% Appraiser Score 96.67% 63.33%

Operator #2 Operator #3 Y/N Y/NTry #2 within known Try #1 Try #2 within known Agree Agree

2 1 0 2 2 1 0 TRUE FALSE1 1 1 1 1 1 1 TRUE TRUE1 1 1 1 1 1 1 TRUE TRUE2 1 0 2 2 1 0 FALSE FALSE1 1 1 2 1 0 0 FALSE FALSE1 1 1 1 1 1 1 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 1 1 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 0 0 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 0 0 1 1 1 1 FALSE FALSE1 1 1 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 0 0 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 FALSE FALSE2 0 0 1 1 1 1 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE2 0 0 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 1 1 1 1 1 1 TRUE TRUE2 1 0 1 1 1 1 FALSE FALSE1 1 1 1 1 1 1 FALSE FALSE2 1 0 1 1 1 1 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE1 0 0 1 1 1 1 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE2 1 0 2 2 1 0 FALSE FALSE1 0 0 2 2 1 0 FALSE FALSE1 1 1 1 1 1 1 TRUE TRUE

76.67% 60.00% 96.67% 66.67%

2 3 2 3 Known-1 Known-2 Known-3TRUE TRUE FALSE FALSE FALSE FALSE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE TRUE FALSE FALSETRUE FALSE TRUE FALSE TRUE TRUE FALSEFALSE FALSE FALSE FALSE FALSE TRUE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUETRUE FALSE TRUE FALSE TRUE TRUE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE FALSE FALSE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE TRUE FALSE TRUEFALSE FALSE FALSE FALSE FALSE TRUE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE TRUE FALSE FALSEFALSE FALSE FALSE FALSE FALSE TRUE TRUEFALSE FALSE FALSE FALSE FALSE FALSE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE TRUE FALSE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUETRUE FALSE FALSE FALSE FALSE FALSE TRUEFALSE FALSE FALSE FALSE FALSE TRUE TRUETRUE FALSE FALSE FALSE FALSE FALSE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE FALSE FALSE TRUETRUE TRUE TRUE TRUE TRUE TRUE TRUEFALSE FALSE FALSE FALSE TRUE FALSE FALSEFALSE FALSE FALSE FALSE FALSE FALSE FALSETRUE TRUE TRUE TRUE TRUE TRUE TRUE

False Neg False Pos Mixed False Neg False Pos Mixed False Neg False Pos0 0 0 0 0 0 0 00 1 0 0 1 0 0 10 1 0 0 1 0 0 10 1 0 0 0 0 0 00 1 0 0 1 0 0 00 0 0 0 1 0 0 10 1 0 0 1 0 0 10 1 0 0 1 0 0 00 1 0 0 1 0 0 10 0 0 0 0 1 0 00 1 0 0 1 0 0 10 1 0 0 0 1 0 10 0 0 0 1 0 0 00 1 0 0 1 0 0 10 1 0 0 0 1 0 00 0 1 0 1 0 0 10 0 0 0 0 1 0 10 1 0 0 1 0 0 10 1 0 0 0 1 0 00 1 0 0 1 0 0 10 1 0 0 1 0 0 10 0 0 0 0 0 0 10 0 0 0 1 0 0 10 0 0 0 0 0 0 10 1 0 0 1 0 0 10 0 0 0 0 1 0 10 1 0 0 1 0 0 10 1 0 0 0 0 0 00 0 0 0 0 1 0 00 1 0 0 1 0 0 1

0 19 1 0 18 7 0 20

Mixed000010000000000000000000000000

1

3/10BMG

pass Demo Datafail Inspection

Operator #1 Operator #2 Operator #3

1 pass pass pass pass pass fail fail2 pass pass pass pass pass fail fail3 fail fail fail fail pass fail fail4 fail fail fail fail fail fail fail5 fail fail fail pass fail fail fail6 pass pass pass pass pass pass pass7 pass fail fail fail fail fail fail8 pass pass pass pass pass pass pass9 fail pass pass pass pass pass pass

10 fail pass pass fail fail fail fail11 pass pass pass pass pass pass pass12 pass pass pass pass pass pass pass13 fail fail fail fail fail fail fail14 fail fail fail pass fail fail fail