Table 2.
Parameters values with noisy data (one experiment)
| |
|
10% |
|
|
|---|---|---|---|---|
| Profile 1 | Profile 2 | Profile 3 | Profile 4 | |
|
f13 |
-0.14 |
-0.27 |
-0.84 |
-0.79 |
|
f21 |
0.26 |
0.47 |
0.4 |
0.29 |
|
f32 |
0.44 |
1 |
0.64 |
0.41 |
|
f41 |
0.04 |
0 |
0.9 |
1 |
|
f53 |
0 |
0.26 |
0.42 |
0.12 |
|
f54 |
-0.06 |
0.04 |
0.1 |
-0.12 |
|
f64 |
0.13 |
0.07 |
1 |
1 |
| Residual |
1.88 |
1.67 |
1.68 |
2.29 |
| |
|
5% |
|
|
| |
Profile 5 |
Profile 6 |
Profile 7 |
Profile 8 |
|
f13 |
-0.282 |
-0.532 |
-0.631 |
-0.893 |
|
f21 |
0.56 |
0.618 |
0.306 |
0.6 |
|
f32 |
1 |
1 |
0.436 |
1 |
|
f41 |
0 |
0.092 |
0.761 |
0.742 |
|
f53 |
0.368 |
0.639 |
0.273 |
0.298 |
|
f54 |
0.127 |
0.244 |
0.021 |
0.279 |
|
f64 |
0.064 |
0.158 |
1 |
1 |
| Residual |
0.4128 |
0.4203 |
0.5706 |
0.4482 |
| |
|
1% |
|
|
| |
Profile 9 |
Profile 10 |
Profile 11 |
Profile 12 |
|
f13 |
-0.881 |
-0.427 |
-0.859 |
-0.71 |
|
f21 |
0.571 |
0.523 |
0.5 |
0.414 |
|
f32 |
0.885 |
0.809 |
0.758 |
0.608 |
|
f41 |
0.587 |
0.078 |
0.661 |
0.656 |
|
f53 |
0.479 |
0.467 |
0.507 |
0.402 |
|
f54 |
0.2 |
0.176 |
0.197 |
0.136 |
|
f64 |
1 |
0.162 |
1 |
1 |
| Residual |
0.0207 |
0.0163 |
0.0167 |
0.0227 |
| |
|
0.5% |
|
|
| |
Profile 13 |
Profile 14 |
Profile 15 |
Profile 16 |
|
f13 |
-0.845 |
-0.744 |
-0.843 |
-0.765 |
|
f21 |
0.535 |
0.472 |
0.496 |
0.453 |
|
f32 |
0.816 |
0.714 |
0.749 |
0.673 |
|
f41 |
0.556 |
0.492 |
0.647 |
0.643 |
|
f53 |
0.492 |
0.439 |
0.497 |
0.443 |
|
f54 |
0.201 |
0.167 |
0.196 |
0.164 |
|
f64 |
0.916 |
0.816 |
1 |
1 |
| Residual | 0.0052 | 0.0041 | 0.0042 | 0.0057 |
We solved a total of 100 problems, each corresponding to a different replication, generated randomly see Additional file 1: Table S1). The table shows the 16 cases for which the residual error is low.