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哎丫丫丫12銅蟲 (初入文壇)
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[求助]
求助？關(guān)于Design Expert軟件操作應(yīng)用中的lack of fit無F值和p值
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關(guān)于Design Expert軟件的應(yīng)用 就是在用Design Expert軟件進(jìn)行數(shù)據(jù)處理后，ANOVA中的lack of fit一項(xiàng)中的F值和p值沒有顯示出來，并且pure error那一項(xiàng)顯示是0，要怎么解決這個(gè)問題？ Use your mouse to right click on individual cells for definitions. Response 1 相對(duì)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-表面溫度 100.72 1 100.72 7.93 0.0259 B-轉(zhuǎn)換點(diǎn)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-表面溫度 -3.55 1 1.26 -6.53 -0.57 1.00 B-轉(zhuǎn)換點(diǎn)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: 相對(duì)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: 相對(duì)干燥能耗 = -110.47085 +0.50586 * 表面溫度 +480.92139 * 轉(zhuǎn)換點(diǎn)含水率 +0.21498 * 微波功率 -0.82280 * 表面溫度 * 轉(zhuǎn)換點(diǎn)含水率 +1.53968E-004 * 表面溫度 * 微波功率 -0.12545 * 轉(zhuǎn)換點(diǎn)含水率 * 微波功率 -9.71335E-003 * 表面溫度^2 -470.72663 * 轉(zhuǎn)換點(diǎn)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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木易95
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