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[求助]
用design expert優(yōu)化制備工藝的問題求解 求統(tǒng)計(jì)高手
第一次用design expert,本身數(shù)學(xué)也不好,摸摸爬爬寫了篇文章,是優(yōu)化制備條件的,選擇最佳溫度,時(shí)間,配比。
給老師看,老師的意見是:“最后數(shù)據(jù)統(tǒng)計(jì)分析還需要補(bǔ)充分析數(shù)據(jù),根據(jù)模型最佳工藝條件應(yīng)該推算出來”。
下圖是他給的表,讓我 補(bǔ)充紅色字體。
![用design expert優(yōu)化制備工藝的問題求解 求統(tǒng)計(jì)高手]()
![用design expert優(yōu)化制備工藝的問題求解 求統(tǒng)計(jì)高手-1]()
我一開始的模型里沒有選擇紅色的這些項(xiàng),因?yàn)槲以嚵撕枚噙x擇,只有這種情況時(shí),p-value,Prob > F顯著,而失擬值Lack of Fit不顯著
![用design expert優(yōu)化制備工藝的問題求解 求統(tǒng)計(jì)高手-2]()
當(dāng)我按照老師的要求修改時(shí),
![用design expert優(yōu)化制備工藝的問題求解 求統(tǒng)計(jì)高手-3]()
方差分析如下:
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 89.81 12 7.48 50.39 0.0009 significant
A-reaction?time 0.12 1 0.12 0.80 0.4223
B-temperature 3.57 1 3.57 24.04 0.0080
C-reactants ratio 2.00 1 2.00 13.49 0.0213
AB 13.85 1 13.85 93.23 0.0006
AC 0.48 1 0.48 3.21 0.1475
BC 0.099 1 0.099 0.67 0.4603
A^2 0.46 1 0.46 3.07 0.1545
B^2 2.16 1 2.16 14.53 0.0189
C^2 11.93 1 11.93 80.33 0.0009
A^2B 10.79 1 10.79 72.66 0.0010
A^2C 0.59 1 0.59 3.99 0.1163
AB^2 2.56 1 2.56 17.23 0.0142
AC^2 0.000 0
B^2C 0.000 0
BC^2 0.000 0
Pure Error 0.59 4 0.15
Cor Total 90.40 16
The Model F-value of 50.39 implies the model is significant. There is only
a 0.09% chance that a "Model F-Value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant.
In this case B, C, AB, B++2+-, C++2+-, A++2+-B, AB++2+- 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. 0.39 R-Squared 0.9934
Mean 2.48 Adj R-Squared 0.9737
C.V. % 15.54 Pred R-Squared N/A
PRESS N/A Adeq Precision 30.433
Case(s) with leverage of 1.0000: Pred R-Squared and PRESS statistic not defined
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your
ratio of 30.433 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 2.78 1 0.17 2.30 3.26
A-reaction?time -0.17 1 0.19 -0.71 0.36 2.00
B-temperature 0.94 1 0.19 0.41 1.48 2.00
C-reactants ratio 0.71 1 0.19 0.17 1.24 2.00
AB -1.86 1 0.19 -2.40 -1.33 1.00
AC -0.35 1 0.19 -0.88 0.19 1.00
BC -0.16 1 0.19 -0.69 0.38 1.00
A^2 0.33 1 0.19 -0.19 0.85 1.01
B^2 0.72 1 0.19 0.19 1.24 1.01
C^2 -1.68 1 0.19 -2.20 -1.16 1.01
A^2B 2.32 1 0.27 1.57 3.08 2.00
A^2C 0.54 1 0.27 -0.21 1.30 2.00
AB^2 -1.13 1 0.27 -1.89 -0.37 2.00
AC++2+- ALIASED A, AB++2+-
B++2+-C ALIASED C, A++2+-C
BC++2+- ALIASED B, A++2+-B
=============================================================
W A R N I N G
The model you selected has terms that are aliased with one another.
If you continue, the least squares parameter estimates for aliased models
will not be unique and the resulting contour plots will be misleading.
=============================================================
Final Equation in Terms of Coded Factors:
electrical conductivity =
+2.78
-0.17 * A
+0.94 * B
+0.71 * C
-1.86 * A * B
-0.35 * A * C
-0.16 * B * C
+0.33 * A^2
+0.72 * B^2
-1.68 * C^2
+2.32 * A^2 * B
+0.54 * A^2 * C
-1.13 * A * B^2
Final Equation in Terms of Actual Factors:
Not available for ALIASED models.
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.
請問這樣的模型可以用嗎? |
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