基于客观性原则，为减少变量问的相关性，本部分采用主成分分析法将7项指标

Based on the principle of objectivity, in order to reduce the relevance of variables, this part adopts the principal component analysis method to convert 7 indicator variables into highly generalized new variables to reflect the social economic environment characteristics of each county (city). After importing the above seven socio-economic environmental indicators into the spssl9.0 version for principal component analysis, the test results of KMO and Bartleet are obtained. The test results are shown in Table 4 and Table 5:

Based on the principle of objectivity, in order to reduce the correlation of variable questions, this part uses the main component analysis method to convert 7 indicator variables into new variables with high generality to reflect the characteristics of the socio-economic environment of each county (district). The above 7 socio-economic environmental indicators are imported into spssl9. After the main component analysis in version 0, the test results of KMO and Bartleet are obtained. The results of the inspection are shown in Tables 4 and 5:

Based on the principle of objectivity, in order to reduce the correlation between variables, this part uses the principal component analysis method to transform seven index variables into new variables with high generality to reflect the social and economic environment characteristics of each county (District). After the above seven socio-economic environmental indicators are imported into spssl9.0 for principal component analysis, kmo and bartleet test results are obtained. See Table 4 and table 5 for inspection results:<br>