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Minitab新手，向各位大神求助下，我用的minitab17进行三因子方差分量计算，怎么没有显示方差分量呀？ 请教下各位，要怎么才能显示出来？帮忙看看哪里弄错了，谢谢！
General Linear Model: 直径 versus 工人, 车床, 轴棒
Method
Factor coding (-1, 0, +1)
Factor Information
Factor Type Levels Values
工人 Fixed 3 A, B, C
车床 Fixed 4 1, 2, 3, 4
轴棒(工人, 车床) Fixed 36 1(A, 1), 2(A, 1), 3(A, 1), 1(A, 2), 2(A, 2), 3(A, 2),
1(A, 3), 2(A, 3), 3(A, 3), 1(A, 4), 2(A, 4), 3(A, 4),
1(B, 1), 2(B, 1), 3(B, 1), 1(B, 2), 2(B, 2), 3(B, 2),
1(B, 3), 2(B, 3), 3(B, 3), 1(B, 4), 2(B, 4), 3(B, 4),
1(C, 1), 2(C, 1), 3(C, 1), 1(C, 2), 2(C, 2), 3(C, 2),
1(C, 3), 2(C, 3), 3(C, 3), 1(C, 4), 2(C, 4), 3(C, 4)
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
工人 2 1.4408 0.72042 47.15 0.000
车床 3 18.5317 6.17722 404.33 0.000
工人*车床 6 0.2058 0.03431 2.25 0.061
轴棒(工人, 车床) 24 0.8267 0.03444 2.25 0.013
Error 36 0.5500 0.01528
Total 71 21.5550
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0.123603 97.45% 94.97% 89.79%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant 23.9750 0.0146 1645.87 0.000
工人
A -0.0625 0.0206 -3.03 0.004 1.33
B 0.1958 0.0206 9.51 0.000 1.33
车床
1 -0.6194 0.0252 -24.55 0.000 1.50
2 0.6639 0.0252 26.31 0.000 1.50
3 -0.3417 0.0252 -13.54 0.000 1.50
工人*车床
A 1 -0.0931 0.0357 -2.61 0.013 2.00
A 2 0.0403 0.0357 1.13 0.266 2.00
A 3 0.0625 0.0357 1.75 0.088 2.00
B 1 0.0986 0.0357 2.76 0.009 2.00
B 2 -0.0014 0.0357 -0.04 0.969 2.00
B 3 -0.0458 0.0357 -1.28 0.207 2.00
轴棒(工人, 车床)
1(A, 1) 0.0000 0.0714 0.00 1.000 1.33
2(A, 1) -0.0000 0.0714 -0.00 1.000 1.33
1(A, 2) -0.0667 0.0714 -0.93 0.356 1.33
2(A, 2) 0.1333 0.0714 1.87 0.070 1.33
1(A, 3) 0.0667 0.0714 0.93 0.356 1.33
2(A, 3) 0.2167 0.0714 3.04 0.004 1.33
1(A, 4) 0.0000 0.0714 0.00 1.000 1.33
2(A, 4) 0.0500 0.0714 0.70 0.488 1.33
1(B, 1) 0.0000 0.0714 0.00 1.000 1.33
2(B, 1) -0.1000 0.0714 -1.40 0.170 1.33
1(B, 2) 0.0167 0.0714 0.23 0.817 1.33
2(B, 2) -0.0333 0.0714 -0.47 0.643 1.33
1(B, 3) 0.1667 0.0714 2.34 0.025 1.33
2(B, 3) -0.0833 0.0714 -1.17 0.251 1.33
1(B, 4) 0.0333 0.0714 0.47 0.643 1.33
2(B, 4) -0.2667 0.0714 -3.74 0.001 1.33
1(C, 1) -0.0667 0.0714 -0.93 0.356 1.33
2(C, 1) -0.0167 0.0714 -0.23 0.817 1.33
1(C, 2) 0.0833 0.0714 1.17 0.251 1.33
2(C, 2) -0.0167 0.0714 -0.23 0.817 1.33
1(C, 3) 0.0167 0.0714 0.23 0.817 1.33
2(C, 3) -0.0333 0.0714 -0.47 0.643 1.33
1(C, 4) -0.1500 0.0714 -2.10 0.043 1.33
2(C, 4) 0.0500 0.0714 0.70 0.488 1.33
Regression Equation
直径 = 23.9750 - 0.0625 工人_A + 0.1958 工人_B - 0.1333 工人_C - 0.6194 车床_1
+ 0.6639 车床_2 - 0.3417 车床_3 + 0.2972 车床_4 - 0.0931 工人*车床_A 1
+ 0.0403 工人*车床_A 2 + 0.0625 工人*车床_A 3 - 0.0097 工人*车床_A 4
+ 0.0986 工人*车床_B 1 - 0.0014 工人*车床_B 2 - 0.0458 工人*车床_B 3
- 0.0514 工人*车床_B 4 - 0.0056 工人*车床_C 1 - 0.0389 工人*车床_C 2
- 0.0167 工人*车床_C 3 + 0.0611 工人*车床_C 4 + 0.0000 轴棒(工人, 车
床)_1(A, 1) - 0.0000 轴棒(工人, 车床)_2(A, 1) + 0.0000 轴棒(工人, 车
床)_3(A, 1) - 0.0667 轴棒(工人, 车床)_1(A, 2) + 0.1333 轴棒(工人, 车
床)_2(A, 2) - 0.0667 轴棒(工人, 车床)_3(A, 2) + 0.0667 轴棒(工人, 车
床)_1(A, 3) + 0.2167 轴棒(工人, 车床)_2(A, 3) - 0.2833 轴棒(工人, 车
床)_3(A, 3) + 0.0000 轴棒(工人, 车床)_1(A, 4) + 0.0500 轴棒(工人, 车
床)_2(A, 4) - 0.0500 轴棒(工人, 车床)_3(A, 4) + 0.0000 轴棒(工人, 车
床)_1(B, 1) - 0.1000 轴棒(工人, 车床)_2(B, 1) + 0.1000 轴棒(工人, 车
床)_3(B, 1) + 0.0167 轴棒(工人, 车床)_1(B, 2) - 0.0333 轴棒(工人, 车
床)_2(B, 2) + 0.0167 轴棒(工人, 车床)_3(B, 2) + 0.1667 轴棒(工人, 车
床)_1(B, 3) - 0.0833 轴棒(工人, 车床)_2(B, 3) - 0.0833 轴棒(工人, 车
床)_3(B, 3) + 0.0333 轴棒(工人, 车床)_1(B, 4) - 0.2667 轴棒(工人, 车
床)_2(B, 4) + 0.2333 轴棒(工人, 车床)_3(B, 4) - 0.0667 轴棒(工人, 车
床)_1(C, 1) - 0.0167 轴棒(工人, 车床)_2(C, 1) + 0.0833 轴棒(工人, 车
床)_3(C, 1) + 0.0833 轴棒(工人, 车床)_1(C, 2) - 0.0167 轴棒(工人, 车
床)_2(C, 2) - 0.0667 轴棒(工人, 车床)_3(C, 2) + 0.0167 轴棒(工人, 车
床)_1(C, 3) - 0.0333 轴棒(工人, 车床)_2(C, 3) + 0.0167 轴棒(工人, 车
床)_3(C, 3) - 0.1500 轴棒(工人, 车床)_1(C, 4) + 0.0500 轴棒(工人, 车
床)_2(C, 4) + 0.1000 轴棒(工人, 车床)_3(C, 4)
Fits and Diagnostics for Unusual Observations
Obs 直径 Fit Resid Std Resid
3 23.0000 23.2000 -0.2000 -2.29 R
4 23.4000 23.2000 0.2000 2.29 R
39 23.5000 23.7000 -0.2000 -2.29 R
40 23.9000 23.7000 0.2000 2.29 R
R Large residual
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发表于 2018-9-26 10:22:17
发表于 2018-9-27 06:43:34
发表于 2019-12-17 19:05:01