Science – Society – Technology

# Table 3 Summary of the second-order linear regression for the regular design: representing the interaction between two factors

Factors $$\beta \,\, \mathrm{value}$$ Standard error t   value Pr ($${>}|$$t|)
(Intercept) −0.027 0.009 −3.037 0.006
Thickness 0.218 0.003 83.748 0.000
Length 0.745 0.003 289.663 0.000
Permeability 0.000 0.003 0.162 0.873
Porosity 0.039 0.003 15.264 0.000
ReservoirDip 0.230 0.003 88.528 0.000
I (thickness$$^{2}$$) −0.007 0.005 −1.277 0.214
I (length$$^{2}$$) −0.050 0.005 −9.904 0.000
I (porosity$$^{2}$$) −0.001 0.005 −0.245 0.809
I (permeability$$^{2}$$) 0.001 0.005 0.216 0.831
I (reservoirDip$$^{2}$$) −0.035 0.005 −6.750 0.000
Thickness: length 0.041 0.005 7.929 0.000
Thickness: porosity 0.007 0.005 1.344 0.192
Thickness: permeability 0.001 0.005 0.220 0.828
Thickness: reservoirDip 0.020 0.005 3.927 0.001
Length: porosity 0.007 0.005 1.316 0.201
Length: permeability 0.002 0.005 0.353 0.727
Length: reservoirDip 0.138 0.005 26.580 0.000
Permeability: porosity 0.001 0.005 0.178 0.861
Porosity: reservoirDip 0.004 0.005 0.734 0.471
Permeability: reservoirDip 0.000 0.005 0.065 0.949
1. Estimate stands for the coefficient value of the regression model. Standard error, t value and p value for each coefficient is given. Gray color shows the important terms that should be retained in the reduced model. The p value is used for selecting the important predictors for the reduced model