the table is data ,y is dependent the next four variables are predictors filter value 1 for trainning 0 testing -1 for validation N Percent Input LayerCovariates x1 x2 x3 x4 Error Function Sum of Squares show how to calculate the folowing out put sse and relative error for each 1,0,-1 group filter

Training	Sum of Squares Error	0.36
	Relative Error	0.045
Testing	Sum of Squares Error	0.166
	Relative Error	0.057
Holdout	Relative Error	0.237
Dependent y

rutting	filter	MLP_PredictedValue_A
8.00	1	7.23
3.40	1	3.37
7.00	-1	6.33
6.80	0	6.38
6.80	1	6.72
3.70	1	3.64
6.00	-1	5.56
3.60	0	3.73
5.80	-1	5.23
3.70	1	3.63
5.70	1	5.60
3.80	1	3.73
4.20	1	4.18
7.20	0	7.23
5.00	1	5.10
4.30	-1	4.17
4.20	1	4.11
5.50	0	5.30
4.80	0	4.82
4.90	-1	4.79
4.80	1	4.61
4.40	0	4.29
4.10	-1	4.31
6.00	1	6.50
4.50	1	4.39
4.20	1	4.31
6.10	1	6.38
5.40	0	5.96
5.00	-1	5.34
5.20	0	5.22
4.75	-1	5.38
5.90	1	6.33