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Given the following experimental output in coded values from a Full Factorial Experiment involving 3 factors (each tested at 2 levels) on the Y output, Process Time. Which of the following would most reasonably be concluded? (Assume alpha = 0.05 and you wish to minimize process time) Factorial Fit: Process Time versus FactorA, FactorB, FactorC Estimated Effects and Coefficients for ProcTime Term Effect Coef SE Coef T P Constant 187 . 656 0 . 2573 729 .29 0 . 000 FactorA 33 . 887 16.944 0. 2573 65 . 85 0. 000 FactorB 0 . 813 0 . 406 0. 2573 1.58 0. 153 Factorc 4. 888 2 . 444 0. 2573 9.50 0. 000 FactorA*FactorB 0.937 0 . 469 0. 2573 1.82 0. 106 FactorA*FactorC 1 . 012 0 . 506 0 . 2573 1.97 0 . 085 FactorB*Factorc 0. 087 0 . 044 0. 2573 0.17 0. 869 FactorA*FactorB*FactorC 0 . 062 0. 031 0. 2573 0 . 12 0. 906 S = 1. 02926 PRESS = 33.9 R-Sq = 99.828 R-Sq (pred) = 99.28% R-Sq (adj) = 99. 668 O a. All of the main effect factors have a statistically significant mean effect on Process Time O b. While statistically significant main effects exist, the model explains relatively little of the variance in process time O c. Factors' A and C have a significant effect on the inherent variance of the response (that is, the mean squared error of the response) O d. To estimate the optimal value for the response one could use the following prediction equation for response optimization Y = 187.66 + 16.9*FactorA + 2.4*FactorC