From the tables above, it is clear that the L models predict the Exports per capita better than the
H values. This is due to the increasing R
2
values from L to H. The coefficients remain significant
in predicting the Y variable.
E. Regression as per the Dummy Variables.
Comments on L
H
0
: The coefficient is not significant in predicting the Exports per capita
H
1
: The coefficient is significant in predicting the Exports per capita(Anon, 2017)
The sig value of the years 1990, 2007 and 2014, 0.000<0.05, we thereby reject the null
hypothesis and conclude that all the coefficients are significant in predicting Exports per capita.
The Constant indicates the values the change in Export per capita whenever the FDI and GDP
per capita is zero. lnFDI per capita indicates the change in Exports per capita for every unit
increase in the FDI per capita all factors held constant. lnGDP per capita indicates the change in
Exports per capita for every unit increase in the FDI per capita all factors held constant(Anon,
2017).
In 1990, R
2
=85.9% indicating that the model explains 85.9% of the Exports per capita. In 2007,
R
2
=90.3% indicating that the model explains 90.3% of the Exports per capita. In 2014,
R
2
=87.7% indicating that the model explains 87.7% of the Exports per capita(Liu et al., 2003).
