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请问design expert 设计结果怎么分析，是否可行？ 发自小木虫Android客户端 |
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Use your mouse to right click on individual cells for definitions. Response 1 温度 ANOVA for Response Surface Linear 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 149.55 2 74.78 17.22 0.0013 significant A-P188 98.84 1 98.84 22.77 0.0014 B-P407 62.43 1 62.43 14.38 0.0053 Residual 34.73 8 4.34 Lack of Fit 34.61 6 5.77 92.28 0.0108 significant Pure Error 0.13 2 0.063 Cor Total 184.28 10 The Model F-value of 17.22 implies the model is significant. There is only a 0.13% 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 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 92.28 implies the Lack of Fit is significant. There is only a 1.08% chance that a "Lack of Fit F-value" this large could occur due to noise. Significant lack of fit is bad -- we want the model to fit. Std. Dev. 2.08 R-Squared 0.8115 Mean 35.33 Adj R-Squared 0.7644 C.V. % 5.90 Pred R-Squared 0.6279 PRESS 68.56 Adeq Precision 10.548 The "Pred R-Squared" of 0.6279 is in reasonable agreement with the "Adj R-Squared" of 0.7644. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 10.548 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 34.89 1 0.65 33.40 36.38 A-P188 3.08 1 0.65 1.59 4.57 1.01 B-P407 -2.66 1 0.70 -4.27 -1.04 1.01 Final Equation in Terms of Coded Factors: 温度 = +34.89 +3.08 * A -2.66 * B Final Equation in Terms of Actual Factors: 温度 = +54.85949 +2.46604 * P188 -1.32795 * P407 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. |
3楼2018-11-30 22:17:20
赵统超
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2楼2018-11-30 20:01:17
4楼2018-11-30 22:18:31
赵统超
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5楼2018-12-01 13:27:10
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