density, the density per square kilometer in each province from year 2000 is included in the model. 7. Empirical Analysis When a regression contains multiple explanatory variables, there is a possibility of some of these being highly correlated. If this is the case, there will be problems with multicollinearity, which in turn may create problems to distinguish the effect of the explanatory variables on the dependent variable. Therefore, to check whether there The are problems with multicollinearity, a correlation matrix was created. matrix (a value showed that density and expenditures were highly correlated with each other of 0.9074). However, the correlation between density and expenditures did not seem to cause any problems to the model and both variables were therefore included in the model.
density, the density per square kilometer in each province from year 2000 is included in the model. 7. Empirical Analysis When a regression contains multiple explanatory variables, there is a possibility of some of these being highly correlated. If this is the case, there will be problems with multicollinearity, which in turn may create problems to distinguish the effect of the explanatory variables on the dependent variable. Therefore, to check whether there are problems with multicollinearity, a correlation matrix was created. The matrix showed that density and expenditures were highly correlated with each other (a value of 0.9074). However, the correlation between density and expenditures did not seem to cause any problems to the model and both variables were therefore included in the model.