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ÎÒÓÃDesign-Expert ×öµÄÏìÓ¦Ãæ·ÖÎö£¬ÓÐÎÊÌâLack of Fit 292.05 ²»ÏÔÖø£¬²»ÖªµÀÔõôÐÞ¸ÄÄ£ÐÍ¡£ Response 1 Y 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 14.81 14 1.06 2.71 0.0360 significant A-P1 0.89 1 0.89 2.27 0.1538 B-T1 2.62 1 2.62 6.73 0.0212 C-P2 0.063 1 0.063 0.16 0.6939 D-t2 0.28 1 0.28 0.73 0.4084 AB 1.27 1 1.27 3.26 0.0927 AC 0.062 1 0.062 0.16 0.6966 AD 6.806E-003 1 6.806E-003 0.017 0.8967 BC 4.08 1 4.08 10.47 0.0060 BD 1.05 1 1.05 2.70 0.1229 CD 3.600E-003 1 3.600E-003 9.237E-003 0.9248 A^2 2.009E-003 1 2.009E-003 5.1 55E-003 0.9438 B^2 4.28 1 4.28 10.97 0.0051 C^2 0.13 1 0.13 0.33 0.5745 D^2 0.16 1 0.16 0.41 0.5305 Residual 5.46 14 0.39 Lack of Fit 5.45 10 0.54 292.05 < 0.0001 significant Pure Error 7.463E-003 4 1.866E-003 Cor Total 20.26 28 The "Lack of Fit F-value" of 292.05 implies the Lack of Fit is significant. There is only a 0.01% 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. 0.62 R-Squared 0.7308 Mean 2.09 Adj R-Squared 0.4615 C.V. % 29.84 Pred R-Squared -0.5493 PRESS 31.40 Adeq Precision 6.582 Y =+26.87383+0.15124 * P1-3.72063 * T1-0.21143 * P2-0.66581 * t2 -0.015020 * P1 * T1-4.14167E-004 * P1 * P2-6.87500E-004 * P1 * t2 +0.020200 * T1 * P2+0.051250 * T1 * t2+3.75000E-004 * P2 * t2 -7.82222E-005 * P1^2+0.12990 * T1^2+3.52250E-004 * P2^2 +9.85313E-003 * t2^2 |
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