Reasonably carry out multivariate analysis:Quantitative data correspondence analysis
DOI:10.11886/scjsws20230814001
English keywords:Correspondence analysis  Factor analysis  Variable transformation  Covariance matrix  Eigenvalue
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Author NameAffiliationAddress
Hu Chunyan Graduate School,Academy of Military Sciences PLA China 北京海淀厢红旗东门外甲1号
Hu Liangping Graduate School,Academy of Military Sciences PLA China 北京海淀厢红旗东门外甲1号
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English abstract:
      The purpose of this article was to introduce the basic concepts, calculation methods, two examples and SAS implementation related to the quantitative data correspondence analysis(QDCA). The basic concepts included variable and sample, explicit variable and latent variable, factor analysis, R-type analysis and Q-type analysis, and correspondence analysis; calculation methods involved the basic principles, variable transformation, construction of R-type and Q-type covariance matrices, and factor analysis; the data in the two examples were "per capita consumption expenditure data of rural households in 10 provinces in China in a certain year" and "frequency of occurrence of various genes of different ethnic groups"; with the help of SAS software, the quantitative data in the two examples were analyzed by QDCA, and the SAS output results were given a reasonable explanation.
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