Question

1. 1 test for multicollinearity and discuss possible solutions.

2. Regression output					confidence interval
   variables	coefficients	std. error	t (df=148)	p-value	95% lower	95% upper	VIF
   Intercept	0.6507
   X1	0.00000662	0.00000074	8.910	1.73E-15	0.00000515	0.00000809	3.860
   X2	0.00041330	0.00023401	1.766	.0794	-0.00004914	0.00087574	1.132
   X3	-0.0006	0.00016086	-3.628	.0004	-0.0009	-0.0003	2.930
   X4	-0.00030420	0.00002572	-11.829	3.82E-23	-0.00035502	-0.00025338	2.654
   X5	0.0550	0.0346	1.587	.1147	-0.0135	0.1234	1.272
   X6	-0.0006	0.00040393	-1.493	.1375	-0.0014	0.0002	3.402
   							2.542
   							mean VIF

Answer 1

Result shows that the independent variable x1,x3 and x4 are significant at 0.05 level of significance because their corresponding p values are less than 0.05 level

Result shows that the independent variable x2,x5 and x6 are insignificant at 0.05 level of significance because their corresponding p values are more than 0.05 level

None of the VIF values are greater than value 10, so there is no signficant multicollinearity which can cause any error in the result.

Therefore, only x1,x3 and x4 are significant independent variables and there is no multicollinearity in the model