在运用主成分分析预测各国接收的难民数量时，对影响各国可接收难民的原始变

When using principal component analysis predicted number of refugees States received, having integrated dimension reduction ability to affect the original variables may receive refugee States, by determining correlation matrix eigenvalues, the feature vector, the extent of losing the original variable information than small. Furthermore, the binding of factor analysis has predicted that the result is more accurate.

In using the main component analysis to predict the number of refugees received by each country, the original variables affecting the acceptance of refugees in each country have comprehensive dimensional ability, and the loss of the original variable information is less when the characteristic value and characteristic vector of the correlation coefficient matrix are judged. In addition, combined with factor analysis prediction makes the results more accurate.

When the principal component analysis is used to predict the number of refugees received by each country, it has a comprehensive dimensionality reduction ability for the original variables that affect the number of refugees received by each country. When the eigenvalues and eigenvectors of the correlation coefficient matrix are judged, the information of the original variables is less lost. In addition, the combination of factor analysis makes the results more accurate.