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Table 3

ANOVA for response surface quadratic model for the prediction of MG removal efficiency

Statistics
Factors (coded)Sum of squares (SS)Degrees of freedom (df)Mean squares (MSS)F-valueProbability (p) > F *PC%
Model 23.46 14 1.68 213.03 < 0.0001 – 
x1 0.73 0.73 92.47 < 0.0001 3.234 
x2 7.02 7.02 891.90 < 0.0001 31.225 
x3 12.30 12.30 1,563.40 < 0.0001 54.711 
x4 0.15 0.15 18.71 0.0010 0.007 
x1 × 2 0.03 0.03 3.50 0.0861 0.122 
x1 × 3 0.08 0.08 10.39 0.0073 0.363 
x1 × 4 0.06 0.06 7.00 0.0213 0.245 
x2 × 3 0.00 0.00 0.03 0.8654 0.001 
x2 × 4 0.02 0.02 2.05 0.1779 0.072 
x3 × 4 0.00 0.00 0.54 0.4780 0.019 
x12 0.04 0.04 5.30 0.0400 0.185 
x22 0.34 0.34 43.31 < 0.0001 1.517 
x32 1.72 1.72 218.46 < 0.0001 7.651 
x42 0.00 0.00 0.03 0.8749 0.001 
Residual 0.09 12 0.01    
Lack of fit 0.09 10 0.01    
Pure error 0.00 0.00    
Cor total 23.56 26     
Statistics
Factors (coded)Sum of squares (SS)Degrees of freedom (df)Mean squares (MSS)F-valueProbability (p) > F *PC%
Model 23.46 14 1.68 213.03 < 0.0001 – 
x1 0.73 0.73 92.47 < 0.0001 3.234 
x2 7.02 7.02 891.90 < 0.0001 31.225 
x3 12.30 12.30 1,563.40 < 0.0001 54.711 
x4 0.15 0.15 18.71 0.0010 0.007 
x1 × 2 0.03 0.03 3.50 0.0861 0.122 
x1 × 3 0.08 0.08 10.39 0.0073 0.363 
x1 × 4 0.06 0.06 7.00 0.0213 0.245 
x2 × 3 0.00 0.00 0.03 0.8654 0.001 
x2 × 4 0.02 0.02 2.05 0.1779 0.072 
x3 × 4 0.00 0.00 0.54 0.4780 0.019 
x12 0.04 0.04 5.30 0.0400 0.185 
x22 0.34 0.34 43.31 < 0.0001 1.517 
x32 1.72 1.72 218.46 < 0.0001 7.651 
x42 0.00 0.00 0.03 0.8749 0.001 
Residual 0.09 12 0.01    
Lack of fit 0.09 10 0.01    
Pure error 0.00 0.00    
Cor total 23.56 26     

*p-values <0.05 were considered to be significant.

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