Factors

Sum of squares

Degrees of freedom

Mean square

Fvalue

Pvalue
 

Model

18368.05

5

3673.61

13.97

0.0016

significant

X
_{1}

740.37

1

740.37

2.82

0.1373
 
X
_{2}

4796.85

1

4796.85

18.24

0.0037
 
X
_{1}
X
_{2}

150.06

1

150.06

0.57

0.4747
 
X
_{1}
^{2}

4654.76

1

4654.76

17.70

0.0040
 
X
_{2}
^{2}

3230.99

1

3230.99

12.29

0.0099
 
Residual

1840.87

7

262.98
   
Lack of fit

1656.83

3

552.28

12.00

0.0181

significant

Pure error

184.04

4

46.01
   
Cor total

20208.92

12
    
 ^{a}Coefficient of determination (R^{2}) = 0.9089. A model with an Fvalue of 13.97 implies that the model is significant. There is only a 0.16% chance that a model Fvalue this large could occur due to noise. Values of “Prob>F” less than 0.0500 indicate that model terms are significant. In this case B, A2, B2 are significant model terms. The “Lack of fit Fvalue” of 12.00 implies that the lack of fit is significant. There is only a 1.81% chance that a lack of fit Fvalue this large could occur due to noise. The “Pred RSquared” of 0.3684 is not as close to the “Adj RSquared” of 0.8438 as one might normally expect. This may indicate a large block effect or a possible problem with a model and/or data. Things to consider are model reduction, response transformation, and outliers, among others. “Adeq Precision” measures the signaltonoise ratio. A ratio greater than 4 is desirable. A ratio of 10.962 indicates an adequate signal. This model can be used to navigate the design space.