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标题:
菜鸟求救,解读结果分析
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作者:
shiyiming
时间:
2010-7-8 16:22
标题:
菜鸟求救,解读结果分析
我用了一个三元二次旋转正交试验,用SAS做了响应面分析出了下面这些方差分析表和方程,我想求教一下论坛上的高手,一次项,二次项,交互项的方差分析是哪个,最佳的条件是怎么计算出来的? 此次试验拟合度好不好? 新手初学,希望有好心人费点劲教下我.
ANOVA for Y1
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Master Model Predictive Model
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Source DF SS MS F Pr > F DF SS MS F Pr > F
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X1 1 0.00101 0.00101 66.31748 0.0001 1 0.00101 0.00101 36.57601 0.0001
X2 1 0.000924 0.000924 60.64407 0.0001 1 0.000924 0.000924 33.44696 0.0001
X3 1 0.000232 0.000232 15.24702 0.00181 1 0.000232 0.000232 8.409176 0.010998
X1*X1 1 0.000201 0.000201 13.22333 0.003015 1 0.000201 0.000201 7.293049 0.01644
X1*X2 1 0.00021 0.00021 13.79501 0.002599
X1*X3 1 6.125E-6 6.125E-6 0.402115 0.536998
X2*X2 1 0.001299 0.001299 85.25696 0.0001 1 0.001299 0.001299 47.02168 0.0001
X2*X3 1 0.000253 0.000253 16.61802 0.00131 1 0.000253 0.000253 9.165318 0.008485
X3*X3 1 0.00151 0.00151 99.11559 0.0001 1 0.00151 0.00151 54.66512 0.0001
Model 9 0.005612 0.000624 40.9353 0.0001 7 0.005395 0.000771 27.90898 0.0001
(Linear) 3 0.002166 0.000722 47.40287 0.0001
Quadratic) 3 0.002976 0.000992 65.13131 0.0001
(Cross Product) 3 0.000469 0.000156 10.27171 0.000973
Error 13 0.000198 0.000015 15 0.000414 0.000028
(Lack of fit) 5 0.000196 0.000039 156.8124 0.0001 7 0.000412 0.000059 235.5803 0.0001
(Pure Error) 8 2E-6 2.5E-7 8 2E-6 2.5E-7
Total 22 0.00581 22 0.00581
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Fit Statistics for Y1
Master Model Predictive Model
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Mean 0.095522 0.095522
R-square 96.59% 92.87%
Adj. R-square 94.23% 89.54%
RMSE 0.003903 0.005255
CV 4.085787 5.501632
Canonical Analysis: Stationary point for Y1
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Stationary point: Critical value is a Maximum
Predicted response at stationary point: 0.115111
Standard error of predicted value: 0.001811
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Canonical Analysis: Critical value for Y1
Factor
Name Coded Uncoded
X1 1.06373 1.06373
X2 -0.25708 -0.25708
X3 0.33345 0.33345
Canonical Analysis: Eigenvectors for Y1
Eigenvalues X1 X2 X3
-0.002486 0.91147 0.39912 -0.09967
-0.007163 0.34374 -0.60581 0.71752
-0.012699 -0.22599 0.68825 0.68937
Ridge Analysis for Y1
Predicted Standard Dependent Type of
Radius Response Error variable ridge X1 X2 X3
0.0 0.10879 .001300088 Y1 MINIMUM 0.00000 0.00000 0.00000
0.1 0.10746 .001299975 Y1 MINIMUM -0.06657 0.06821 -0.03026
0.2 0.10597 .001300081 Y1 MINIMUM -0.12994 0.14198 -0.05439
0.3 0.10431 .001301729 Y1 MINIMUM -0.19032 0.22088 -0.07063
0.4 0.10249 .001307118 Y1 MINIMUM -0.24779 0.30431 -0.07742
0.5 0.10048 .001319256 Y1 MINIMUM -0.30229 0.39139 -0.07368
0.6 0.09828 .001341823 Y1 MINIMUM -0.35374 0.48102 -0.05908
0.7 0.09589 .001378902 Y1 MINIMUM -0.40208 0.57198 -0.03416
0.8 0.09328 .001434585 Y1 MINIMUM -0.44742 0.66318 -0.00013
0.9 0.09045 .001512526 Y1 MINIMUM -0.48997 0.75380 0.04149
1.0 0.08740 .001615559 Y1 MINIMUM -0.53001 0.84330 0.08915
0.0 0.10879 .001300088 Y1 MAXIMUM 0.00000 0.00000 0.00000
0.1 0.10998 .001299976 Y1 MAXIMUM 0.07015 -0.06227 0.03466
0.2 0.11102 .001300081 Y1 MAXIMUM 0.14437 -0.11816 0.07208
0.3 0.11193 .001301729 Y1 MAXIMUM 0.22319 -0.16713 0.11071
0.4 0.11270 .001307118 Y1 MAXIMUM 0.30706 -0.20854 0.14910
0.5 0.11336 .001319256 Y1 MAXIMUM 0.39625 -0.24172 0.18589
0.6 0.11389 .001341824 Y1 MAXIMUM 0.49072 -0.26616 0.21991
0.7 0.11432 .001378902 Y1 MAXIMUM 0.58998 -0.28165 0.25021
0.8 0.11465 .001434586 Y1 MAXIMUM 0.69318 -0.28844 0.27624
0.9 0.11488 .001512527 Y1 MAXIMUM 0.79925 -0.28722 0.29783
1.0 0.11503 .001615560 Y1 MAXIMUM 0.90711 -0.27900 0.31515
Alias Structure for Y1
Master Model Predictive Model
No effects aliased. No effects aliased.
Predictive Model for Y1
Coded Levels(-1,1):
Y1 = 0.108792 + 0.0086*X1 - 0.008224*X2 + 0.004124*X3 - 0.00356*X1*X1
- 0.00904*X2*X2 - 0.005625*X2*X3 - 0.009748*X3*X3
Uncoded Levels:
Y1 = 0.108792 + 0.0086*X1 - 0.008224*X2 + 0.004124*X3 - 0.00356*X1*X1
- 0.00904*X2*X2 - 0.005625*X2*X3 - 0.009748*X3*X3
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Effect Estimates for Y1
Master Model Predictive Model
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Term Estimate Std Err t Pr > |t| Estimate Std Err t Pr > |t|
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X1 0.0086004 0.001056 8.143555 0.0001 0.0086004 0.001422 6.047811 0.0001
X2 -0.008224 0.001056 -7.78743 0.0001 -0.008224 0.001422 -5.78333 0.0001
X3 0.0041238 0.001056 3.904744 0.00181 0.0041238 0.001422 2.899858 0.010998
X1*X1 -0.00356 0.000979 -3.63639 0.003015 -0.00356 0.001318 -2.70056 0.01644
X1*X2 0.005125 0.00138 3.714163 0.002599
X1*X3 0.000875 0.00138 0.634125 0.536998
X2*X2 -0.00904 0.000979 -9.23347 0.0001 -0.00904 0.001318 -6.85724 0.0001
X2*X3 -0.005625 0.00138 -4.07652 0.00131 -0.005625 0.001858 -3.02743 0.008485
X3*X3 -0.009748 0.000979 -9.95568 0.0001 -0.009748 0.001318 -7.39359 0.0001
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