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Table 6

Analysis of variance (ANOVA) and regression analysis for the quadratic model for the operational parameters

Analysis of variance (ANOVA)
Model formula in RSM (X1, X2, X3, X4)DFSum of squaresMean squaresF-valueProbability (P)
FO 6501.6 2167.21 223.7129 <2.2 × 10−16 
TWI 182.6 60.86 6.2828 0.0009967 
PQ 81.2 27.08 2.7956 0.0490435 
Residuals 53 513.8 9.69 – – 
Lack of fit 17 207.3 12.20 1.4345 0.1774642 
Pure error 36 306.1 8.50 – – 
Regression analysis for the quadratic model
ParameterCoefficient estimate
Std. errort valuePr(>|t|)
(Intercept) 72.05621 0.74533 96.6772 <2.20 × 10−16 
X1 −19.1321 0.87453 −21.8771 <2.20 × 10−16 
X2 −10.5902 0.87453 −12.1096 <2.20 × 10−16 
X3 −3.25333 0.48026 −6.774 1.04 × 10−8 
X1 × X2 4.0365 1.61729 2.4958 0.015712 
X1 × X3 2.32317 1.07107 2.169 0.034588 
X2 × X3 −3.01325 1.07107 −2.8133 0.006864 
X22 −3.31457 1.17894 −2.8115 0.006898 
Analysis of variance (ANOVA)
Model formula in RSM (X1, X2, X3, X4)DFSum of squaresMean squaresF-valueProbability (P)
FO 6501.6 2167.21 223.7129 <2.2 × 10−16 
TWI 182.6 60.86 6.2828 0.0009967 
PQ 81.2 27.08 2.7956 0.0490435 
Residuals 53 513.8 9.69 – – 
Lack of fit 17 207.3 12.20 1.4345 0.1774642 
Pure error 36 306.1 8.50 – – 
Regression analysis for the quadratic model
ParameterCoefficient estimate
Std. errort valuePr(>|t|)
(Intercept) 72.05621 0.74533 96.6772 <2.20 × 10−16 
X1 −19.1321 0.87453 −21.8771 <2.20 × 10−16 
X2 −10.5902 0.87453 −12.1096 <2.20 × 10−16 
X3 −3.25333 0.48026 −6.774 1.04 × 10−8 
X1 × X2 4.0365 1.61729 2.4958 0.015712 
X1 × X3 2.32317 1.07107 2.169 0.034588 
X2 × X3 −3.01325 1.07107 −2.8133 0.006864 
X22 −3.31457 1.17894 −2.8115 0.006898 

F-statistic: 77.6 on 9 and 53 DF, p-value: <2.2 × 10−16, Multiple R2:0.9295, Adjusted R2: 0.9175, Predicted R2: 0.9126, Lack of fit: 0.1774642.

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