R&r Atributo
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- Pages: 19
Instructions
Attribute Gage R & R Effectiveness Instructions: 1) 2)
3)
4)
5) 6) 7) 8)
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. In the Data Entry worksheet fill in the appropriate information in the Scoring Report section an enter the type of Attributes you are evaluating in the Attribute Legend section. YOU MUST EN THE INFORMATION IN THE ATTRIBUTE LEGEND SECTION OR THE SPREADSH 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. If you or an expert has selected samples to be evaluated and you know what attributes these samples are, enter this information in the Attribute sample column. This will enable you to det 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. 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. the attributes must be spelled properly or the spreadsheet will not analyze the data correc 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 icon To see a Demo of the Attribute GR&R Effectiveness spreadsheet, click on the Demo Move around the spread sheet to see the data. When you are finished click the Delete All 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.
Page 1
Instructions
With a worksheet limitation of 100 samples, at best a lower 95% limit of 96.4% can be calculat Thus, we would have to say that an inspector could be as bad as 96% efficient, even though he/s missed no calls. Sample Size # Matches 95% UCL Calculated Score 95% LCL
100 < Try out different combinations of number of samples and number 100 < to see the effects of sample size. In this case, a sample size of 3 100.0% < one non-match will yield a 17% confidence interval. In order to ge 100.0% < reliability in estimates of efficiency, large sample sizes will be req 96.4%
Page 2
Instructions
ctiveness, in which up to
coring Report section and ection. YOU MUST ENTER N OR THE SPREADSHEET g. Yes, No; pass, fail; pell properly. what attributes these is will enable you to determine tandard. You do not k although you will
you will be evaluating cific operator. Remember nalyze the data correctly.
on the Demo icon. ick the Delete All icon
ence limits on the ported on the report nterval represents nty associated with
eliable estimates of score for Operator 1 ough there was a 100% core, Operators 1 and 3 #2 (78.6%) lies within
Page 3
Instructions
f 96.4% can be calculated. ficient, even though he/she
samples and number of matches se, a sample size of 30 with interval. In order to get reasonable mple sizes will be required.
Page 4
Data Entry
Attribute Gage R & R Effective SCORING REPORT Attribute Legend5 (used in computations) 1 pass 2 fail
Known Population Sample # Attribute 1 pass 2 pass 3 fail 4 fail 5 fail 6 pass 7 pass 8 pass 9 fail 10 fail 11 pass 12 pass 13 fail 14 fail 15 pass 16 pass 17 fail 18 fail 19 fail 20 pass 21 pass 22 pass 23 fail 24 fail 25 pass 26 pass 27 fail 28 fail 29 pass
Operator #1 Try #1 Try #2 pass pass pass pass fail fail fail fail fail fail pass pass fail fail pass pass pass pass pass pass pass pass pass pass fail fail fail fail pass pass pass pass fail fail fail fail fail fail pass pass fail fail pass pass pass pass pass pass pass pass pass pass fail fail fail fail pass pass
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DATE: NAME: PRODUCT: BUSINESS: Operator #2 Try #1 pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass
Data Entry
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
pass
pass
Page 6
pass
pass
Data Entry
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
% APPRAISER SCORE(1) -> % SCORE VS. ATTRIBUTE(2) ->
100.00% 24.00%
SCREEN % E 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 (5) Enter Pass/Fail, Good/Bad, Accept/Reject or other labels which indicate sta
Page 7
Data Entry
& R Effectiveness
ORING REPORT Allied Employee 3313 Spark Plug F&SP
Operator #2 Try #2 pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass
All operators agree within and
All Operators
between each
agree with
Other
standard
Y/N Agree N N N Y N Y Y Y Y N Y Y Y N N N N Y N Y Y Y Y N Y Y Y N N
Y/N Agree N N N Y N Y N Y N N Y Y Y N N N N Y N Y N Y N N Y Y Y N N
Operator #3 Try #1 Try #2 fail fail fail fail fail fail fail fail fail fail pass pass fail fail pass pass pass pass fail fail pass pass pass pass fail fail fail fail fail fail fail fail fail fail fail fail fail fail pass pass fail fail pass pass pass pass fail fail pass pass pass pass fail fail fail fail fail fail
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Data Entry
pass
fail
fail
N
Page 9
N
Data Entry
80.00% 20.00%
100.00% 20.00%
16.00% SCREEN % EFFECTIVE SCORE(3) -> SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE (4) ->
s AND agreed with the known standard abels which indicate status of inspection
Page 10
12.00%
Report
Attribute Gage R & R Effectiveness SCORING REPORT DATE: 12/30/1899 NAME: Allied Employee PRODUCT: 3313 Spark Plug BUSINESS: F&SP
Attribute Legend 1 pass 2 fail Known Population Sample # Attribute 1 pass 2 pass 3 fail 4 fail 5 fail 6 pass 7 pass 8 pass 9 fail 10 fail 11 pass 12 pass 13 fail 14 fail 15 pass 16 pass 17 fail 18 fail 19 fail 20 pass 21 pass 22 pass 23 fail 24 fail 25 pass 26 pass 27 fail 28 fail 29 pass 30 pass 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 -
Operator #1 Try #1 Try #2 pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass -
% APPRAISER SCORE(1) -> % SCORE VS. ATTRIBUTE(2) ->
pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass -
Operator #2 Try #1 Try #2 pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass -
100.00% 24.00%
pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass -
All operators agree within and All Operators between each agree with Other standard
Operator #3 Try #1 Try #2 fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail -
80.00% 20.00%
fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail -
Y/N Agree N N N Y N Y Y Y Y N Y Y Y N N N N Y N Y Y Y Y N Y Y Y N N N
Y/N Agree N N N Y N Y N Y N N Y Y Y N N N N Y N Y N Y N N Y Y Y N N N
100.00% 20.00%
SCREEN % EFFECTIVE SCORE(3) -> 16.00% SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE(4) -> 12.00% 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 Page 11
Statistical Report - Attribute Gage R&R Study DATE: 12/30/1899 NAME: Allied Employee PRODUCT: 3313 Spark Plug BUSINESS: F&SP % Appraiser1 Operator #1 Operator #2 Operator #3 30 30 30 30 24 30
Source Total Inspected # Matched False Negative (operator biased toward rejection) False Positive (operator biased toward acceptance) Mixed 100.0% 92.3% 95% UCL 100.0% 80.0% Calculated Score 88.4% 61.4% 95% LCL
100.0% 100.0% 88.4%
Screen % Effective Score3 100 16
Total Inspected # in Agreement 95% UCL Calculated Score 95% LCL
2 4 0 92.3% 80.0% 61.4%
24.7% 16.0% 9.4%
% Score vs Appraiser
100.0%
100.0%
90.0%
90.0%
80.0%
80.0%
50.0%
95% UCL Calculated Score
40.0%
95% LCL
% Efficiency
110.0%
60.0%
70.0% 50.0%
95% UCL Calculated Score
40.0%
95% LCL
60.0%
30.0%
30.0%
20.0%
20.0%
10.0%
10.0%
0.0%
8 2 0 82.7% 66.7% 47.2%
20.0% 12.0% 6.4%
110.0%
70.0%
2 2 6 82.7% 66.7% 47.2%
Screen % Effective Score vs Attribute4 100 12
% Appraiser
% Efficiency
%Score vs Attribute2 Operator #1 Operator #2 Operator #3 30 30 30 24 20 20
0.0% Operator #1
Operator #2
Operator #3
Operator #1
Operator #2
Operator #3
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
Calculations
Known Population Sample # Attribute 1 1 2 1 3 2 4 2 5 2 6 1 7 1 8 1 9 2 10 2 11 1 12 1 13 2 14 2 15 1 16 1 17 2 18 2 19 2 20 1 21 1 22 1 23 2 24 2 25 1 26 1 27 2 28 2 29 1 30 1 31 32 33 34 35 36 37 38 39 40 41 42
Operator #1 Try #1 Try #2 1 1 1 1 2 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1
Page 13
Operator #2 Try #1 Try #2 1 1 1 1 2 1 2 2 1 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 1 2 1 1 1 1 2 1 2 2 1 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 1 2 1 1 1 1
Operator #3 Try #1 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2
Calculations
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94
Page 14
Calculations
95 96 97 98 99 100 % Appraiser Score
100.00%
Page 15
80.00%
Calculations
Operator #3 Try #2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2
Y/N Agree FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
Y/N Agree FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
Page 16
Calculations
Page 17
Calculations
100.00%
Page 18
Sheet5
Page 19
Attribute Gage R & R Effectiveness Instructions: 1) 2)
3)
4)
5) 6) 7) 8)
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. In the Data Entry worksheet fill in the appropriate information in the Scoring Report section an enter the type of Attributes you are evaluating in the Attribute Legend section. YOU MUST EN THE INFORMATION IN THE ATTRIBUTE LEGEND SECTION OR THE SPREADSH 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. If you or an expert has selected samples to be evaluated and you know what attributes these samples are, enter this information in the Attribute sample column. This will enable you to det 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. 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. the attributes must be spelled properly or the spreadsheet will not analyze the data correc 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 icon To see a Demo of the Attribute GR&R Effectiveness spreadsheet, click on the Demo Move around the spread sheet to see the data. When you are finished click the Delete All 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.
Page 1
Instructions
With a worksheet limitation of 100 samples, at best a lower 95% limit of 96.4% can be calculat Thus, we would have to say that an inspector could be as bad as 96% efficient, even though he/s missed no calls. Sample Size # Matches 95% UCL Calculated Score 95% LCL
100 < Try out different combinations of number of samples and number 100 < to see the effects of sample size. In this case, a sample size of 3 100.0% < one non-match will yield a 17% confidence interval. In order to ge 100.0% < reliability in estimates of efficiency, large sample sizes will be req 96.4%
Page 2
Instructions
ctiveness, in which up to
coring Report section and ection. YOU MUST ENTER N OR THE SPREADSHEET g. Yes, No; pass, fail; pell properly. what attributes these is will enable you to determine tandard. You do not k although you will
you will be evaluating cific operator. Remember nalyze the data correctly.
on the Demo icon. ick the Delete All icon
ence limits on the ported on the report nterval represents nty associated with
eliable estimates of score for Operator 1 ough there was a 100% core, Operators 1 and 3 #2 (78.6%) lies within
Page 3
Instructions
f 96.4% can be calculated. ficient, even though he/she
samples and number of matches se, a sample size of 30 with interval. In order to get reasonable mple sizes will be required.
Page 4
Data Entry
Attribute Gage R & R Effective SCORING REPORT Attribute Legend5 (used in computations) 1 pass 2 fail
Known Population Sample # Attribute 1 pass 2 pass 3 fail 4 fail 5 fail 6 pass 7 pass 8 pass 9 fail 10 fail 11 pass 12 pass 13 fail 14 fail 15 pass 16 pass 17 fail 18 fail 19 fail 20 pass 21 pass 22 pass 23 fail 24 fail 25 pass 26 pass 27 fail 28 fail 29 pass
Operator #1 Try #1 Try #2 pass pass pass pass fail fail fail fail fail fail pass pass fail fail pass pass pass pass pass pass pass pass pass pass fail fail fail fail pass pass pass pass fail fail fail fail fail fail pass pass fail fail pass pass pass pass pass pass pass pass pass pass fail fail fail fail pass pass
Page 5
DATE: NAME: PRODUCT: BUSINESS: Operator #2 Try #1 pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass
Data Entry
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
pass
pass
Page 6
pass
pass
Data Entry
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
% APPRAISER SCORE(1) -> % SCORE VS. ATTRIBUTE(2) ->
100.00% 24.00%
SCREEN % E 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 (5) Enter Pass/Fail, Good/Bad, Accept/Reject or other labels which indicate sta
Page 7
Data Entry
& R Effectiveness
ORING REPORT Allied Employee 3313 Spark Plug F&SP
Operator #2 Try #2 pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass
All operators agree within and
All Operators
between each
agree with
Other
standard
Y/N Agree N N N Y N Y Y Y Y N Y Y Y N N N N Y N Y Y Y Y N Y Y Y N N
Y/N Agree N N N Y N Y N Y N N Y Y Y N N N N Y N Y N Y N N Y Y Y N N
Operator #3 Try #1 Try #2 fail fail fail fail fail fail fail fail fail fail pass pass fail fail pass pass pass pass fail fail pass pass pass pass fail fail fail fail fail fail fail fail fail fail fail fail fail fail pass pass fail fail pass pass pass pass fail fail pass pass pass pass fail fail fail fail fail fail
Page 8
Data Entry
pass
fail
fail
N
Page 9
N
Data Entry
80.00% 20.00%
100.00% 20.00%
16.00% SCREEN % EFFECTIVE SCORE(3) -> SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE (4) ->
s AND agreed with the known standard abels which indicate status of inspection
Page 10
12.00%
Report
Attribute Gage R & R Effectiveness SCORING REPORT DATE: 12/30/1899 NAME: Allied Employee PRODUCT: 3313 Spark Plug BUSINESS: F&SP
Attribute Legend 1 pass 2 fail Known Population Sample # Attribute 1 pass 2 pass 3 fail 4 fail 5 fail 6 pass 7 pass 8 pass 9 fail 10 fail 11 pass 12 pass 13 fail 14 fail 15 pass 16 pass 17 fail 18 fail 19 fail 20 pass 21 pass 22 pass 23 fail 24 fail 25 pass 26 pass 27 fail 28 fail 29 pass 30 pass 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 -
Operator #1 Try #1 Try #2 pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass -
% APPRAISER SCORE(1) -> % SCORE VS. ATTRIBUTE(2) ->
pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass fail fail fail pass fail pass pass pass pass pass fail fail pass pass -
Operator #2 Try #1 Try #2 pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass fail fail pass pass fail pass pass fail pass pass fail pass pass pass -
100.00% 24.00%
pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass pass fail fail pass fail pass pass fail pass pass fail fail pass pass -
All operators agree within and All Operators between each agree with Other standard
Operator #3 Try #1 Try #2 fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail -
80.00% 20.00%
fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail fail fail fail pass fail pass pass fail pass pass fail fail fail fail -
Y/N Agree N N N Y N Y Y Y Y N Y Y Y N N N N Y N Y Y Y Y N Y Y Y N N N
Y/N Agree N N N Y N Y N Y N N Y Y Y N N N N Y N Y N Y N N Y Y Y N N N
100.00% 20.00%
SCREEN % EFFECTIVE SCORE(3) -> 16.00% SCREEN % EFFECTIVE SCORE vs. ATTRIBUTE(4) -> 12.00% 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 Page 11
Statistical Report - Attribute Gage R&R Study DATE: 12/30/1899 NAME: Allied Employee PRODUCT: 3313 Spark Plug BUSINESS: F&SP % Appraiser1 Operator #1 Operator #2 Operator #3 30 30 30 30 24 30
Source Total Inspected # Matched False Negative (operator biased toward rejection) False Positive (operator biased toward acceptance) Mixed 100.0% 92.3% 95% UCL 100.0% 80.0% Calculated Score 88.4% 61.4% 95% LCL
100.0% 100.0% 88.4%
Screen % Effective Score3 100 16
Total Inspected # in Agreement 95% UCL Calculated Score 95% LCL
2 4 0 92.3% 80.0% 61.4%
24.7% 16.0% 9.4%
% Score vs Appraiser
100.0%
100.0%
90.0%
90.0%
80.0%
80.0%
50.0%
95% UCL Calculated Score
40.0%
95% LCL
% Efficiency
110.0%
60.0%
70.0% 50.0%
95% UCL Calculated Score
40.0%
95% LCL
60.0%
30.0%
30.0%
20.0%
20.0%
10.0%
10.0%
0.0%
8 2 0 82.7% 66.7% 47.2%
20.0% 12.0% 6.4%
110.0%
70.0%
2 2 6 82.7% 66.7% 47.2%
Screen % Effective Score vs Attribute4 100 12
% Appraiser
% Efficiency
%Score vs Attribute2 Operator #1 Operator #2 Operator #3 30 30 30 24 20 20
0.0% Operator #1
Operator #2
Operator #3
Operator #1
Operator #2
Operator #3
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
Calculations
Known Population Sample # Attribute 1 1 2 1 3 2 4 2 5 2 6 1 7 1 8 1 9 2 10 2 11 1 12 1 13 2 14 2 15 1 16 1 17 2 18 2 19 2 20 1 21 1 22 1 23 2 24 2 25 1 26 1 27 2 28 2 29 1 30 1 31 32 33 34 35 36 37 38 39 40 41 42
Operator #1 Try #1 Try #2 1 1 1 1 2 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1
Page 13
Operator #2 Try #1 Try #2 1 1 1 1 2 1 2 2 1 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 1 2 1 1 1 1 2 1 2 2 1 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 1 2 1 1 1 1
Operator #3 Try #1 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2
Calculations
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94
Page 14
Calculations
95 96 97 98 99 100 % Appraiser Score
100.00%
Page 15
80.00%
Calculations
Operator #3 Try #2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2
Y/N Agree FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
Y/N Agree FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
Page 16
Calculations
Page 17
Calculations
100.00%
Page 18
Sheet5
Page 19
