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4.4.3 Statistical Criteria
There are several tests that can be used to determine the suitability of the statistically derived regression model.
a. F-Test
The F-test is a statistical test used to determine the effect of independent variables on the dependent variable as a whole. The first step in performing the F-
test is to determining and writing the hypotheses. H
: β1 = β2 = ... = βt = 0 No independent variables that affect the dependent variable
H
1
: at least one βt ≠ 0 At least one of the independent variables significantly influences the
dependent variable. 1. If the F-statistic significance level α, then reject H
and it conclude that at least one independent variable affects the dependent variable.
2. If the F-statistic significance level α, then accept H and conclude that
there are no independent variables that affect the dependent variable.
b. T-Test
The T-test is a statistical test used to measure whether the parameters of the equation are individually significant or not, and is also known as a partial test
of significance because the significance of each variable can be observed in the model. A T-test is used in this study to determine the effect of each explanatory
factor to the three main exporters of natural rubber. The first step to performing a t-test is determining and writing the hypotheses.
H : βt = 0 to t = 1,2,3, ...., N
H
1
: ≠ 0 β
t
If the t-statistic obtained on the real level of α is greater than in the t-table t-statistic t-table, then H
is rejected. Rejection of the H � = 0 implies that
the variables tested significantly affect the dependent variable. Conversely, if the t-statistic is less than in the t-table t-statistic t-table on the real level of α, then
H is accepted. Accepting H
β = 0 indicates that the variables tested did not significantly affect the dependent variable. A smaller α implies further risk
reduction. The result of the model is expected to be better with each additional independent variable that has a significant effect on the dependent variable.
c.
R
2
and adj-R
2
Test
The R
2
and the adjusted R
2
adj-R
2
are used to determine whether the variables in the model can explain the variation that occurs in the independent
variable The higher the R
2
or adj-R
2
, the better the result of the model. In econometric practice, the use of the adj-R
2
value is preferred to the use of R
2
because adj-R
2
tends to give a better overview of the results of the regression. This is especially true when there are a large number of independent variables in
the model, or the number is close to the total number of observations Gujarati, 2011.