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Log-linear analysis : ウィキペディア英語版
Log-linear analysis
Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.〔)
==Fitting criterion==
Log-linear analysis uses a likelihood ratio statistic \Chi^2 :〔 that has an approximate chi-square distribution when the sample size is large:
:\Chi^2 = 2\sum O_ \ln \frac},
where
:\ln = natural logarithm;
:O_ = observed frequency in cell''ij'' (''i'' = row and ''j'' = column);
:E_ = expected frequency in cell''ij''.
:\Chi^2 = the deviance for the model.〔

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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