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发表于 2011-3-30 20:05:34
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一 结果分析1
Use your mouse to right click on individual cells for definitions.
Response 1 Viable cells
ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 241.98 6 40.33 39.74 0.0005 significant
A-Ethanolamine 55.77 1 55.77 54.96 0.0007
F-Hydrocortisone 51.46 1 51.46 50.71 0.0008
H-Lipoic aciid 12.55 1 12.55 12.36 0.0170
J-Lipid mixture 84.75 1 84.75 83.52 0.0003
K-Dummy 1 13.85 1 13.85 13.65 0.0141
L-Dummy 2 23.60 1 23.60 23.26 0.0048
Residual 5.07 5 1.01
Cor Total 247.05 11
The Model F-value of 39.74 implies the model is significant. There is only
a 0.05% chance that a "Model F-Value" this large could occur due to noise.
Values of " rob > F" less than 0.0500 indicate model terms are significant.
In this case A, F, H, J, K, L are significant model terms.
Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms (not counting those required to support hierarchy),
model reduction may improve your model.
Std. Dev. 1.01 R-Squared 0.9795
Mean 13.66 Adj R-Squared 0.9548
C.V. % 7.38 Pred R-Squared 0.8817
PRESS 29.22 Adeq Precision 18.730
The "red R-Squared" of 0.8817 is in reasonable agreement with the "Adj R-Squared" of 0.9548.
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your
ratio of 18.730 indicates an adequate signal. This model can be used to navigate the design space.
Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF
Intercept 13.66 1 0.29 12.91 14.40
A-Ethanolamine -2.16 1 0.29 -2.90 -1.41 1.00
F-Hydrocortisone -2.07 1 0.29 -2.82 -1.32 1.00
H-Lipoic aciid -1.02 1 0.29 -1.77 -0.27 1.00
J-Lipid mixture 2.66 1 0.29 1.91 3.41 1.00
K-Dummy 1 -1.07 1 0.29 -1.82 -0.33 1.00
L-Dummy 2 1.40 1 0.29 0.65 2.15 1.00
Final Equation in Terms of Coded Factors:
Viable cells =
+13.66
-2.16 * A
-2.07 * F
-1.02 * H
+2.66 * J
-1.07 * K
+1.40 * L
Final Equation in Terms of Actual Factors:
Viable cells =
+16.24750
-1.72467 * Ethanolamine
-8.28333 * Hydrocortisone
-4.09000 * Lipoic aciid
+5.31500 * Lipid mixture
-1.07417 * Dummy 1
+1.40250 * Dummy 2
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node.
In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:
1) Normal probability plot of the studentized residuals to check for normality of residuals.
2) Studentized residuals versus predicted values to check for constant error.
3) Externally Studentized Residuals to look for outliers, i.e., influential values.
4) Box-Cox plot for power transformations.
If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.
分析：虚拟项也为显著因素
R-Squared 0.9795
Adj R-Squared 0.9548
Pred R-Squared 0.8817
二 将两个虚拟项设置为误差项，得到结果分析2
Use your mouse to right click on individual cells for definitions.
Response 1 Viable cells
ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 191.98 3 63.99 9.30 0.0055 significant
A-Ethanolamine 55.77 1 55.77 8.10 0.0216
F-Hydrocortisone 51.46 1 51.46 7.48 0.0257
J-Lipid mixture 84.75 1 84.75 12.31 0.0080
Residual 55.07 8 6.88
Cor Total 247.05 11
The Model F-value of 9.30 implies the model is significant. There is only
a 0.55% chance that a "Model F-Value" this large could occur due to noise.
Values of "rob > F" less than 0.0500 indicate model terms are significant.
In this case A, F, J are significant model terms.
Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms (not counting those required to support hierarchy),
model reduction may improve your model.
Std. Dev. 2.62 R-Squared 0.7771
Mean 13.66 Adj R-Squared 0.6935
C.V. % 19.21 Pred R-Squared 0.4985
PRESS 123.91 Adeq Precision 9.089
The "red R-Squared" of 0.4985 is in reasonable agreement with the "Adj R-Squared" of 0.6935.
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your
ratio of 9.089 indicates an adequate signal. This model can be used to navigate the design space.
Coefficient Standard 95% CI 95% CI
Factor Estimate df Error Low High VIF
Intercept 13.66 1 0.76 11.91 15.40
A-Ethanolamine -2.16 1 0.76 -3.90 -0.41 1.00
F-Hydrocortisone -2.07 1 0.76 -3.82 -0.32 1.00
J-Lipid mixture 2.66 1 0.76 0.91 4.40 1.00
Final Equation in Terms of Coded Factors:
Viable cells =
+13.66
-2.16 * A
-2.07 * F
+2.66 * J
Final Equation in Terms of Actual Factors:
Viable cells =
+15.22500
-1.72467 * Ethanolamine
-8.28333 * Hydrocortisone
+5.31500 * Lipid mixture
The Diagnostics Case Statistics Report has been moved to the Diagnostics Node.
In the Diagnostics Node, Select Case Statistics from the View Menu.
Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:
1) Normal probability plot of the studentized residuals to check for normality of residuals.
2) Studentized residuals versus predicted values to check for constant error.
3) Externally Studentized Residuals to look for outliers, i.e., influential values.
4) Box-Cox plot for power transformations.
If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.
分析：得到三个显著因素，但是
R-Squared 0.7771
Adj R-Squared 0.6935
Pred R-Squared 0.4985
请大家帮忙分析一下原因，请问第二个分析结果可用吗？
这个实验是不是存在很大的误差啊？ |
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发表于 2011-3-29 18:52:34
发表于 2011-3-29 22:40:58