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小李茂刚新虫 (正式写手)
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[求助]
响应面实验结果，麻烦哪位大神帮我仔细分析一下，麻烦谢谢！
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Use your mouse to right click on individual cells for definitions. Response 1 转化率 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 0.81 9 0.090 401.47 < 0.0001 significant A-浓度 0.056 1 0.056 251.16 < 0.0001 B-液固比 0.62 1 0.62 2771.53 < 0.0001 C-温度 4.599E-003 1 4.599E-003 20.62 0.0027 AB 0.027 1 0.027 119.37 < 0.0001 AC 1.692E-004 1 1.692E-004 0.76 0.4126 BC 4.260E-003 1 4.260E-003 19.10 0.0033 A^2 6.821E-004 1 6.821E-004 3.06 0.1238 B^2 0.094 1 0.094 421.55 < 0.0001 C^2 3.538E-003 1 3.538E-003 15.86 0.0053 Residual 1.561E-003 7 2.230E-004 Lack of Fit 1.132E-003 3 3.772E-004 3.51 0.1282 not significant Pure Error 4.297E-004 4 1.074E-004 Cor Total 0.81 16 The Model F-value of 401.47 implies the model is significant. There is only a 0.01% 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, AB, BC, 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. The "Lack of Fit F-value" of 3.51 implies the Lack of Fit is not significant relative to the pure error. There is a 12.82% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good -- we want the model to fit. Std. Dev. 0.015 R-Squared 0.9981 Mean 0.79 Adj R-Squared 0.9956 C.V. % 1.90 Pred R-Squared 0.9767 PRESS 0.019 Adeq Precision 65.140 The "Pred R-Squared" of 0.9767 is in reasonable agreement with the "Adj R-Squared" of 0.9956. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 65.140 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 0.84 1 6.679E-003 0.82 0.85 A-浓度 0.084 1 5.280E-003 0.071 0.096 1.00 B-液固比 0.28 1 5.280E-003 0.27 0.29 1.00 C-温度 -0.024 1 5.280E-003 -0.036 -0.011 1.00 AB -0.082 1 7.467E-003 -0.099 -0.064 1.00 AC 6.505E-003 1 7.467E-003 -0.011 0.024 1.00 BC 0.033 1 7.467E-003 0.015 0.050 1.00 A^2 0.013 1 7.278E-003 -4.482E-003 0.030 1.01 B^2 -0.15 1 7.278E-003 -0.17 -0.13 1.01 C^2 0.029 1 7.278E-003 0.012 0.046 1.01 Final Equation in Terms of Coded Factors: 转化率 = +0.84 +0.084 * A +0.28 * B -0.024 * C -0.082 * A * B +6.505E-003 * A * C +0.033 * B * C +0.013 * A^2 -0.15 * B^2 +0.029 * C^2 Final Equation in Terms of Actual Factors: 转化率 = +0.46813 -0.016262 * 浓度 +0.33861 * 液固比 -0.019780 * 温度 -5.09902E-003 * 浓度 * 液固比 +8.13091E-005 * 浓度 * 温度 +4.07953E-004 * 液固比 * 温度 +7.95503E-004 * 浓度^2 -9.33946E-003 * 液固比^2 +7.24667E-005 * 温度^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.@月只蓝@beefly |
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