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[求助]
谁能教教我怎么对响应面的结果进行分析 已有7人参与
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我的响应面做完了，但是其中的失拟项后面没有P值和F值，因此我就不能确定其的显著性，我想知道这是为什么，我已经做了2次了，望大家赐教。急啊~~~~~~ 谢谢了 |
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23楼2014-02-21 21:34:32
行板如歌
金虫 (小有名气)
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2楼2012-12-03 16:34:39
3楼2012-12-05 22:27:09
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Use your mouse to right click on individual cells for definitions. Response 1 浸提液中花青素质量浓度 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 0.49 9 0.054 21.41 0.0003 significant A-X1 时间 3.916E-003 1 3.916E-003 1.55 0.2530 B-X2 温度 0.028 1 0.028 11.17 0.0124 C-X3 料液比 0.027 1 0.027 10.85 0.0132 AB 0.088 1 0.088 34.95 0.0006 AC 7.832E-003 1 7.832E-003 3.10 0.1215 BC 0.011 1 0.011 4.16 0.0807 A^2 0.11 1 0.11 45.41 0.0003 B^2 0.12 1 0.12 48.21 0.0002 C^2 0.052 1 0.052 20.46 0.0027 Residual 0.018 7 2.524E-003 Lack of Fit 0.018 3 5.890E-003 Pure Error 0.000 4 0.000 Cor Total 0.50 16 The Model F-value of 21.41 implies the model is significant. There is only a 0.03% 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, A++2+-, 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. 0.050 R-Squared 0.9649 Mean 5.50 Adj R-Squared 0.9199 C.V. % 0.91 Pred R-Squared 0.4391 PRESS 0.28 Adeq Precision 14.663 The "Pred R-Squared" of 0.4391 is not as close to the "Adj R-Squared" of 0.9199 as one might normally expect. This may indicate a large block effect or a possible problem with your model and/or data. Things to consider are model reduction, response tranformation, outliers, etc. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 14.663 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 5.71 1 0.022 5.65 5.76 A-X1 时间 0.022 1 0.018 -0.020 0.064 1.00 B-X2 温度 0.059 1 0.018 0.017 0.10 1.00 C-X3 料液比 0.059 1 0.018 0.016 0.10 1.00 AB -0.15 1 0.025 -0.21 -0.089 1.00 AC 0.044 1 0.025 -0.015 0.10 1.00 BC -0.051 1 0.025 -0.11 8.150E-003 1.00 A^2 -0.17 1 0.024 -0.22 -0.11 1.01 B^2 -0.17 1 0.024 -0.23 -0.11 1.01 C^2 -0.11 1 0.024 -0.17 -0.053 1.01 Final Equation in Terms of Coded Factors: 浸提液中花青素质量浓度 = +5.71 +0.022 * A +0.059 * B +0.059 * C -0.15 * A * B +0.044 * A * C -0.051 * B * C -0.17 * A^2 -0.17 * B^2 -0.11 * C^2 Final Equation in Terms of Actual Factors: 浸提液中花青素质量浓度 = -6.13650 +2.79225 * X1 时间 +0.52027 * X2 温度 +0.11635 * X3 料液比 -0.059400 * X1 时间 * X2 温度 +8.85000E-003 * X1 时间 * X3 料液比 -1.02500E-003 * X2 温度 * X3 料液比 -0.66000 * X1 时间^2 -6.80000E-003 * X2 温度 ^2 -1.10750E-003 * X3 料液比^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. |
4楼2012-12-05 22:30:09
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