Table 9 Statistical parameters and regression models for FN(G).
From: On machine learning based QSPR analysis of amphetamine derivatives using regression models
Property | Mode | Equation | R | \(R^2\) | \(S_E\) | F | p-value |
|---|---|---|---|---|---|---|---|
BP | Quadratic | \(y=271.5008-0.1824x+0.0003x^2\) | 0.928 | 0.861 | 0.2012 | 37.1195 | 0.000 |
Cubic | \(y=-216.8704+1.5685x-0.0017x^2+0.0000x^3\) | 0.943 | 0.889 | 1.0736 | 29.2566 | 0.000 | |
EV | Quadratic | \(y=58.9171-0.0404x+0.0000x^2\) | 0.940 | 0.884 | 0.0226 | 45.6948 | 0.000 |
Cubic | \(y=-12.2928+0.2149x-0.0002x^2+0.0000x^3\) | 0.961 | 0.923 | 0.1099 | 43.9129 | 0.000 | |
FP | Quadratic | \(y=160.2920-0.2141x+0.0002x^2\) | 0.917 | 0.841 | 0.1317 | 31.8469 | 0.000 |
Cubic | \(y=30.9558+0.2495x-0.0003x^2+0.0000x^3\) | 0.920 | 0.847 | 0.7729 | 20.2427 | 0.000 | |
MR | Quadratic | \(y=25.2626+0.0568x-0.0000x^2\) | 0.751 | 0.564 | 0.0401 | 7.7554 | 0.007 |
Cubic | \(y=-4.9299+0.1651x-0.0001x^2+0.0000x^3\) | 0.756 | 0.572 | 0.2370 | 4.9039 | 0.021 | |
SA | Quadratic | \(y=84.7684-0.2012x+0.0002x^2\) | 0.817 | 0.667 | 0.1195 | 12.0094 | 0.001 |
Cubic | \(y=116.6979-0.3156x+0.0003x^2-0.0000x^3\) | 0.817 | 0.668 | 0.7118 | 7.3658 | 0.006 | |
P | Quadratic | \(y=9.9888+0.0226x-0.0000x^2\) | 0.751 | 0.564 | 0.0158 | 7.7662 | 0.007 |
Cubic | \(y=-2.0248+0.0657x-0.0001x^2+0.0000x^3\) | 0.757 | 0.573 | 0.0935 | 4.9133 | 0.021 | |
ST | Quadratic | \(y=41.7226-0.0315x+0.0000x^2\) | 0.785 | 0.616 | 0.0489 | 9.6237 | 0.003 |
Cubic | \(y=50.1215-0.0616x+0.0001x^2-0.0000x^3\) | 0.785 | 0.616 | 0.2916 | 5.8907 | 0.012 | |
MV | Quadratic | \(y=79.8866+0.2134x-0.0001x^2\) | 0.528 | 0.279 | 0.1524 | 2.3248 | 0.140 |
Cubic | \(y=-28.2484+0.6011x-0.0005x^2+0.0000x^3\) | 0.540 | 0.292 | 0.9016 | 1.5089 | 0.267 |
