Having estimated model, variables considered
are Real Gross Domestic Product (dependent variable), Government Expenditure on
Health and Government Expenditure on Education (Independent variables) it
covers the period of years: 1980-2008
PRESENTATION
OF RESULTS
GDP = + 2.2287 + 0.17246GEH
+ 2.299GEE
S.E = (1.2671) (1.5560) (0.9999)
t0.025 =
F (2,
26) = 95.65
F0.05 =
3.37
R2 = 0.8804
DW =
0.762
ANALYSIS
OF RESULTS
(a) T-test:
It is used to test for the statistical significance of the individual estimated
parameters. The calculated t-value for the regression coefficients of GEH and
GEE are 0.11 and 2.210 respectively. Since the caculated t-value of GEE is
greathan than the tabulated t-value at 5% level of significance; we conclude
that the regression coefficient is statistically significant. However, the regression
coefficient of GEH is not statistically
insignificant.
(b) Standard
Error test: It is used to test
for statistical reliability of the coefficient estimates.
S(b1)
= 1.5560 S(b2)
= 0.9999
b1/2 =
0.08623 b2/2 = 1.104595
Since
S(b1)
< b1/2, we conclude that the coefficient estimate of b1
is statistically significant. However, coefficient estimate of GEE is statistically significant.
(c)
F-Test:
This is used to test for the joint influence of the explanatory variables on
the dependent variable. The F-calculated
value is 95.652 while the F-tabulated value is 3.37 at 5% level of significance. Since
the F-calculated value is
greater than the F-tabulated
value, we conclude that the entire regression plane is statistically
significant. This means that the joint influence of the explanatory variables (GEE
and GEH) on the dependent variable (GDP) is statistically significant. This
result can as well be confirmed from the F- probability which is statistically
significant.
(d)
Coefficient
of Determination (R2):
It is used to measure the proportion of variations in the dependent variable,
which is explained by the explanatory variables. The computed coefficient of
determination (R2= 0.88804) shows that 88.04% of the
total variations in the dependent variable LGDP) is influenced by the variation
in the explanatory variables namely Government Expenditure on Health (GEH) and Government
Expenditure on Education (GEE) while 11.95%
of the total variation in the dependent variable is attributable to the
influence of other factors not included in the regression model.
(e)
Durbin
Watson statistics: It is used to test for the presence of positive first
order serial correlation. The computed DW is 0.762. At 5% level of significance with two explanatory variables
and 29 observations, the tabulated DW for dL and du are 1.270 and 1.563
respectively. The value of DW is less than the lower limit. Therefore, we
conclude that there is evidence of positive first order serial correlation
TEST OF HYPOTHESIS
F-test
is employed in testing the hypothesis. Using 5% level of significance at 2/26
degrees of freedom, the F-tabulated
value is 3.37 while calculated
F-value is 95.652. Since the
calculated F-value is greater than the tabulated F-value Ho is. Thus, Hi is
accepted on the proposition that Human Capital Development had significant
impact on Economic growth in Nigeria
within the period under study i.e. 1980-2008.
IMPLICATION OF THE RESULT
The regression result shows that there existed a positive
relationship between dependent variable (RGDP) and the explanatory variables
(GEH and GEE). It is estimated from the result that a unit increase in government
expenditure on health and government expenditure on education, on the average,
will lead to increase by 0.17 and 2.21 units in GDP respectively. However,
holding the explanatory variables constant, GDP increase by 2.23 units. It is
obviously seen that the sign borne by parameters estimate meet the prior expectations.
The entire repression plan is statistical
significant. Invariably, the joint information of the explanatory variables has
a significant influence on the dependent variable. Also, the coefficient of multiple
determinations shows a valid goodness of fit. However, there is presence of
autocorrelation. This could be attribute to the absence or omission of some variable
which are captured in the stochastic variables but not included in the
regression model.