Table 7 Adjusted linear regression model for the 2 6 factorial design following log data transformation - Rosenbrock function
From: Factorial design analysis applied to the performance of parallel evolutionary algorithms
Estimate | Standard error | t value | Pr(> | t|) | |
|---|---|---|---|---|
(Intercept) | 1.338 | 0.0035 | 385.842 | <2e -16 |
Top | 0.098 | 0.0035 | 28.141 | <2e -16 |
Pop | 0.096 | 0.0035 | 27.677 | <2e -16 |
Rate | -0.095 | 0.0035 | -27.481 | <2e -16 |
Sel | 0.075 | 0.0035 | 21.558 | <2e -16 |
Nindv | 0.029 | 0.0035 | 8.223 | 5.98e -13 |
Proc | 0.018 | 0.0035 | 5.114 | 1.45e -06 |
Rate:Sel | -0.054 | 0.0035 | -15.616 | <2e -16 |
Top:Sel | 0.021 | 0.0035 | 5.998 | 2.93e -08 |
Top:Proc | 0.066 | 0.0035 | 19.125 | <2e -16 |
Pop:Proc | 0.055 | 0.0035 | 15.785 | <2e -16 |
Rate:Proc | -0.041 | 0.0035 | -11.726 | <2e -16 |
Top:Rate | -0.017 | 0.0035 | -5.039 | 1.98e -06 |
Top:Pop | -0.014 | 0.0035 | -4.045 | 0.0001 |
Sel:Proc | 0.014 | 0.0035 | 3.940 | 0.0001 |
Top:Nindv | -0.013 | 0.0035 | -3.662 | 0.0004 |
Rate:Nindv | -0.012 | 0.0035 | -3.511 | 0.0007 |
Sel:Nindv | 0.010 | 0.0035 | 2.740 | 0.0072 |
Pop:Rate | 0.008 | 0.0035 | 2.308 | 0.0230 |
Top:Rate:Sel | -0.015 | 0.0035 | -4.305 | 3.80e -05 |
Rate:Sel:Proc | -0.012 | 0.0035 | -3.522 | 0.0006 |
Top:Pop:Proc | -0.011 | 0.0035 | -3.075 | 0.0027 |
Top:Sel:Nindv | -0.010 | 0.0035 | -2.934 | 0.0041 |
Top:Rate:Proc | -0.008 | 0.0035 | -2.183 | 0.0313 |