Descriptive Statistics

1. Descriptive Statistics:

2. Correlation Matrix:

Multicollinearity is the condition occurring when two or more of the independent variables in a regression equation are correlated.

There are certain reasons of multicollinearity occurs:

* It is caused by an inaccurate use of dummy variables.

* It is caused by the inclusion of a variable which is computed from other variables in the data set.

* Multicollinearity can also result from the repetition of the same kind of variable.

* Generally occurs when the variables are highly correlated to each other.

Our table shows us only between LNHIGHTECH and LNIMPORT have multicollinearity. Because the value is over 0,8(0,882720).

COVARIANCE ANALYSIS

According to our table, we have 0,828277 R-squared value. This means, almost 83% of the model can be explained by those variables. The adjusted R-squared value is 0,826577. If one more variable is added to model and adjusted R-squared is increased, the variable supposed to be added. If adjusted R-squared decreases with new variable, it should not be added.

When we look at F-test, our Probability of F-statistic is zero. So that, we should reject null hypothesis which is below;

H0: ß1 = ß2 = ß3 = ß4 = …. = 0

H1 : Otherwise

So, we can say that all coefficients are different from each other and zero.

Regression Model :

LNHIGHTECH= -7.45 + 0,077LNGDP + 0,066LNFDI + 1,04LNIMPORT + 0,528LNRD - 0,195LNUNEMP

Additionaly if probability of any variable is more than 0,10 it is insignificant. If it is lower than 0,01 % of confidence interval is significant. If the probability is between the values of 0,01 and 0,05, 5% confidence of interval is significant. Also, if the probability is between 0,05 and 0,10, 10% confidence of interval is significant. When we look at the probability, we see that all variables are significant. That means, all independent variables have effect on High Technology Exports. We can also say that, if we increase LNGDP by 1 unit...