求助---GR&R分析的数据差异（结果）？？
88.8+/-0.4mm,选择10 个产品安排3个人各测3次，用公司EXCEL表格（应该用的是极差的算法）与用MINITAB结果却不相同，请教下
1.用极差算法与minitbab的算法在公式上有什么区别
2.两种方式的ndc的结果却不一样
3.两种方式的GR&R的结论也不一样
4.minitab的各项标示什么意思
-----------------------------------------------------------------------------------------------------------------------
EXCEL结果
见附件
-------------------------------------------------------------------------------------------------------------------------
MINITAB的结果
（包括附件的图）
Gage R&R
Gage R&R Study - ANOVA Method
Two-Way ANOVA Table With Interaction
Source DF SS MS F P
part 9 0.267201 0.0296890 512.753 0.000
op 2 0.000980 0.0004900 8.463 0.003
part * op 18 0.001042 0.0000579 0.008 1.000
Repeatability 60 0.429067 0.0071511
Total 89 0.698290
Alpha to remove interaction term = 0.25
Two-Way ANOVA Table Without Interaction
Source DF SS MS F P
part 9 0.267201 0.0296890 5.38409 0.000
op 2 0.000980 0.0004900 0.08886 0.915
Repeatability 78 0.430109 0.0055142
Total 89 0.698290
Gage R&R
%Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0055142 67.24
Repeatability 0.0055142 67.24
Reproducibility 0.0000000 0.00
op 0.0000000 0.00
Part-To-Part 0.0026861 32.76
Total Variation 0.0082003 100.00
Process tolerance = 0.8
Study Var %Study Var %Tolerance
Source StdDev (SD) (6 * SD) (%SV) (SV/Toler)
Total Gage R&R 0.0742578 0.445547 82.00 55.69
Repeatability 0.0742578 0.445547 82.00 55.69
Reproducibility 0.0000000 0.000000 0.00 0.00
op 0.0000000 0.000000 0.00 0.00
Part-To-Part 0.0518275 0.310965 57.23 38.87
Total Variation 0.0905555 0.543333 100.00 67.92
Number of Distinct Categories = 1
Gage R&R for Dim1
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原始数据：
1 1 88.69
1 1 88.79
1 1 88.85
2 1 88.79
2 1 88.66
2 1 88.80
3 1 88.81
3 1 88.94
3 1 88.96
4 1 88.82
4 1 88.68
4 1 88.78
5 1 88.84
5 1 88.78
5 1 88.66
6 1 88.79
6 1 88.80
6 1 88.93
7 1 88.95
7 1 88.81
7 1 88.69
8 1 88.77
8 1 88.84
8 1 88.80
9 1 88.65
9 1 88.81
9 1 88.79
10 1 88.94
10 1 88.96
10 1 88.80
1 2 88.70
1 2 88.80
1 2 88.85
2 2 88.80
2 2 88.66
2 2 88.81
3 2 88.81
3 2 88.93
3 2 88.96
4 2 88.82
4 2 88.69
4 2 88.79
5 2 88.86
5 2 88.80
5 2 88.65
6 2 88.81
6 2 88.80
6 2 88.93
7 2 88.94
7 2 88.83
7 2 88.69
8 2 88.78
8 2 88.85
8 2 88.81
9 2 88.65
9 2 88.80
9 2 88.81
10 2 88.94
10 2 88.95
10 2 88.81
1 3 88.69
1 3 88.80
1 3 88.85
2 3 88.81
2 3 88.66
2 3 88.81
3 3 88.81
3 3 88.94
3 3 88.95
4 3 88.81
4 3 88.69
4 3 88.79
5 3 88.84
5 3 88.80
5 3 88.65
6 3 88.82
6 3 88.81
6 3 88.95
7 3 88.95
7 3 88.81
7 3 88.70
8 3 88.80
8 3 88.86
8 3 88.80
9 3 88.66
9 3 88.81
9 3 88.82
10 3 88.94
10 3 88.96
10 3 88.83
1.用极差算法与minitbab的算法在公式上有什么区别
2.两种方式的ndc的结果却不一样
3.两种方式的GR&R的结论也不一样
4.minitab的各项标示什么意思
-----------------------------------------------------------------------------------------------------------------------
EXCEL结果
见附件
-------------------------------------------------------------------------------------------------------------------------
MINITAB的结果
（包括附件的图）
Gage R&R
Gage R&R Study - ANOVA Method
Two-Way ANOVA Table With Interaction
Source DF SS MS F P
part 9 0.267201 0.0296890 512.753 0.000
op 2 0.000980 0.0004900 8.463 0.003
part * op 18 0.001042 0.0000579 0.008 1.000
Repeatability 60 0.429067 0.0071511
Total 89 0.698290
Alpha to remove interaction term = 0.25
Two-Way ANOVA Table Without Interaction
Source DF SS MS F P
part 9 0.267201 0.0296890 5.38409 0.000
op 2 0.000980 0.0004900 0.08886 0.915
Repeatability 78 0.430109 0.0055142
Total 89 0.698290
Gage R&R
%Contribution
Source VarComp (of VarComp)
Total Gage R&R 0.0055142 67.24
Repeatability 0.0055142 67.24
Reproducibility 0.0000000 0.00
op 0.0000000 0.00
Part-To-Part 0.0026861 32.76
Total Variation 0.0082003 100.00
Process tolerance = 0.8
Study Var %Study Var %Tolerance
Source StdDev (SD) (6 * SD) (%SV) (SV/Toler)
Total Gage R&R 0.0742578 0.445547 82.00 55.69
Repeatability 0.0742578 0.445547 82.00 55.69
Reproducibility 0.0000000 0.000000 0.00 0.00
op 0.0000000 0.000000 0.00 0.00
Part-To-Part 0.0518275 0.310965 57.23 38.87
Total Variation 0.0905555 0.543333 100.00 67.92
Number of Distinct Categories = 1
Gage R&R for Dim1
---------------------------------------------------------------------------------------------------------------------------------
原始数据：
1 1 88.69
1 1 88.79
1 1 88.85
2 1 88.79
2 1 88.66
2 1 88.80
3 1 88.81
3 1 88.94
3 1 88.96
4 1 88.82
4 1 88.68
4 1 88.78
5 1 88.84
5 1 88.78
5 1 88.66
6 1 88.79
6 1 88.80
6 1 88.93
7 1 88.95
7 1 88.81
7 1 88.69
8 1 88.77
8 1 88.84
8 1 88.80
9 1 88.65
9 1 88.81
9 1 88.79
10 1 88.94
10 1 88.96
10 1 88.80
1 2 88.70
1 2 88.80
1 2 88.85
2 2 88.80
2 2 88.66
2 2 88.81
3 2 88.81
3 2 88.93
3 2 88.96
4 2 88.82
4 2 88.69
4 2 88.79
5 2 88.86
5 2 88.80
5 2 88.65
6 2 88.81
6 2 88.80
6 2 88.93
7 2 88.94
7 2 88.83
7 2 88.69
8 2 88.78
8 2 88.85
8 2 88.81
9 2 88.65
9 2 88.80
9 2 88.81
10 2 88.94
10 2 88.95
10 2 88.81
1 3 88.69
1 3 88.80
1 3 88.85
2 3 88.81
2 3 88.66
2 3 88.81
3 3 88.81
3 3 88.94
3 3 88.95
4 3 88.81
4 3 88.69
4 3 88.79
5 3 88.84
5 3 88.80
5 3 88.65
6 3 88.82
6 3 88.81
6 3 88.95
7 3 88.95
7 3 88.81
7 3 88.70
8 3 88.80
8 3 88.86
8 3 88.80
9 3 88.66
9 3 88.81
9 3 88.82
10 3 88.94
10 3 88.96
10 3 88.83
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fornewjob (威望:0) (江苏 苏州) 机械制造 工程师
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1 1 88.69
1 1 88.68
1 1 88.69
2 1 88.79
2 1 88.78
2 1 88.77
3 1 88.85
3 1 88.84
3 1 88.84
4 1 88.79
4 1 88.78
4 1 88.80
5 1 88.66
5 1 88.66
5 1 88.65
6 1 88.80
6 1 88.79
6 1 88.81
7 1 88.81
7 1 88.80
7 1 88.79
8 1 88.94
8 1 88.93
8 1 88.94
9 1 88.96
9 1 88.95
9 1 88.96
10 1 88.82
10 1 88.81
10 1 88.80
1 2 88.70
1 2 88.69
1 2 88.69
2 2 88.80
2 2 88.79
2 2 88.78
3 2 88.85
3 2 88.86
3 2 88.85
4 2 88.80
4 2 88.80
4 2 88.81
5 2 88.66
5 2 88.65
5 2 88.65
6 2 88.81
6 2 88.81
6 2 88.80
7 2 88.81
7 2 88.80
7 2 88.81
8 2 88.93
8 2 88.93
8 2 88.94
9 2 88.96
9 2 88.94
9 2 88.95
10 2 88.82
10 2 88.83
10 2 88.81
1 3 88.69
1 3 88.69
1 3 88.70
2 3 88.80
2 3 88.79
2 3 88.80
3 3 88.85
3 3 88.84
3 3 88.86
4 3 88.81
4 3 88.80
4 3 88.80
5 3 88.66
5 3 88.65
5 3 88.66
6 3 88.81
6 3 88.82
6 3 88.81
7 3 88.81
7 3 88.81
7 3 88.82
8 3 88.94
8 3 88.95
8 3 88.94
9 3 88.95
9 3 88.95
9 3 88.96
10 3 88.81
10 3 88.81
10 3 88.83