the predictive features of the second-order

BMC 　 BMC-SDS　 BMC　　 BMC-SDS 　 Vd BMC	mobile phase　 BMC BMC-SDS CL BMC BMC-SDS BMC 　 BMC-SDS	n	a±ts_a	b±ts_b	c±ts_c	r²	S.E.	F	RMSEC	RMSECV	RMSECVi
			(p-value)	(p-value)	(p-value)	r²_adj.		(p-value)
t_1/2	Brij35	7	52.42±5.225	-95.56±4.737	44.28±4.579	0.9453	0.9047	34.590	0.6968	0.7388	0.5425
			0.0064	0.0091	0.0102	0.9180		0.0029
	Brij35-SDS	7	65.93±10.35	-107.6±9.506	43.94±9.279	0.9824	0.5133	111.68	0.3880	0.4191	0.3733
			0.0004	0.0007	0.0008	0.9736		0.0003
t_max	Brij35	6	8.586±2.601	-14.26±2.088	7.647±2.231	0.9323	0.2598	27.546	0.1808	0.5053	0.1980
			0.0499	0.1050	0.0895	0.8985		0.0046
	Brij35-SDS	6	15.29±5.319	-24.52±4.673	11.31±4.953	0.9742	0.1853	56.583	0.3583	0.3928	0.4249
			0.0129	0.0185	0.0158	0.9570		0.0041

Table 4: The statistical analysis and the predictive features of the second-order polynomial models using BMC_Brij35 and BMC_{Brij35: SDS=85:15} systems, respectively.

Goto home»