Inc = Income Level
Br = The Total Recreation fee to Pangandaran Beach
Fk = Visit Frequency kali
E = Error Term
H. Classical Assumption Test 1.
Multicollinearity Test.
Multicollinearity test was conducted to test whether the regression model found any correlation between the independent variables.
Otherwise there is a problem multicollinearity, if there is a correlation. A good regression model should not happen correlation between
independent variables. How to detect there is multicollinearity or not in the regression model are:
a. R
2
is quite high 0,7-0,1, but the t-test for each regression coefficient was not significant.
b. The high of R
2
is a sufficient condition, but not a necessary condition for the multicollinearity. Because in a low R
2
0,5 can also occur multicollinearity.
c. Regressing variable independent X with another independent variable, then its R
2
calculated by using F test; If F F table its mean H
o
is rejected, and there is a multicollinearity. If F F table its mean H
o
is accepted, and there is no multicollinearity.
The problem of multicollinearity can also be seen on the value of tolerance and the variance inflation factor VIF in the regression
analysis in SPSS16. If the tolerance value is greater than 0.1 and less than 10 VIF then there is no multicollinearity problem.
2. Heteroskedasticity Test.
Heteroskedasticity test aims to test whether the regression model occurred inequality residual variance from one observation to another
observation. If the variance of the residuals of the observations to other observations is remains, it is called homoskedasticity and if the
variance of the residuals of the observations to other observations different is called heteroskedasticity. A good regression model is that
has homokedasticity or did not happend heteroskedasticity. How to detect the presence or absence of heteroskedasticity is
using glejser test. General principle decision whether there is or not heteroskedasticity is see significant value after glejser test, if the
significant value 0.05, this data can be said homoskedasticity or not exposed heteroskedasticity, and vice versa if the significance value
0.05 then the data is exposed heteroskedasticity Sari, 2015.
3. Normality Test.
Normality test on the regression model was used to test whether the residual value is normally distributed or not. A good regression
model is one that has residual value that is normally distributed.
