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注册: 2015-12-14
专业: 制造系统与自动化

[求助] 求助？关于Design Expert软件操作应用中的lack of fit无F值和p值

关于Design Expert软件的应用
就是在用Design Expert软件进行数据处理后，ANOVA中的lack of fit一项中的F值和p值没有显示出来，并且pure error那一项显示是0，要怎么解决这个问题？
Use your mouse to right click on individual cells for definitions.
Response 1 相对干燥能耗
      ANOVA for Response Surface Quadratic 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 581.32 9 64.59 5.09 0.0217 significant
  A-表面温度 100.72 1 100.72 7.93 0.0259
  B-转换点含水率 86.84 1 86.84 6.84 0.0347
  C-微波功率 79.28 1 79.28 6.24 0.0411
  AB 0.68 1 0.68 0.053 0.8240
  AC 0.095 1 0.095 7.468E-003 0.9336
  BC 25.18 1 25.18 1.98 0.2019
  A^2 0.25 1 0.25 0.020 0.8927
  B^2 93.30 1 93.30 7.35 0.0302
  C^2 177.71 1 177.71 14.00 0.0073
Residual 88.88 7 12.70
Lack of Fit 88.88 3 29.63
Pure Error 0.000 4 0.000
Cor Total 670.20 16

The Model F-value of 5.09 implies the model is significant.  There is only
a 2.17% 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 A, B, C, B++2+-, C++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. 3.56 R-Squared 0.8674
Mean 28.49 Adj R-Squared 0.6969
C.V. % 12.51 Pred R-Squared -1.1220
PRESS 1422.15 Adeq Precision 6.382

A negative "Pred R-Squared" implies that the overall mean is a better predictor of your
response than the current model.

"Adeq Precision" measures the signal to noise ratio.  A ratio greater than 4 is desirable.  Your
ratio of 6.382 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 33.88 1 1.59 30.11 37.65
A-表面温度 -3.55 1 1.26 -6.53 -0.57 1.00
B-转换点含水率 3.29 1 1.26 0.32 6.27 1.00
C-微波功率 -3.15 1 1.26 -6.13 -0.17 1.00
AB -0.41 1 1.78 -4.62 3.80 1.00
AC 0.15 1 1.78 -4.06 4.37 1.00
BC -2.51 1 1.78 -6.72 1.70 1.00
A^2 -0.24 1 1.74 -4.35 3.86 1.01
B^2 -4.71 1 1.74 -8.81 -0.60 1.01
C^2 -6.50 1 1.74 -10.60 -2.39 1.01

Final Equation in Terms of Coded Factors:

相对干燥能耗    =
+33.88
-3.55    * A
+3.29    * B
-3.15    * C
-0.41    * A * B
+0.15    * A * C
-2.51    * B * C
-0.24    * A^2
-4.71    * B^2
-6.50    * C^2

Final Equation in Terms of Actual Factors:

相对干燥能耗    =
-110.47085
+0.50586    * 表面温度
+480.92139    * 转换点含水率
+0.21498    * 微波功率
-0.82280    * 表面温度 * 转换点含水率
+1.53968E-004    * 表面温度 * 微波功率
-0.12545    * 转换点含水率 * 微波功率
-9.71335E-003    * 表面温度^2
-470.72663    * 转换点含水率^2
-1.62417E-004    * 微波功率^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.

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