ロジスティック回帰
A word of caution is in order when interpreting pseudo-² statistics. The reason these indices of fit are referred to as "pseudo" ² is that they do not represent the proportionate reduction in error as the ² in linear regression does. Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression will always be heteroscedastic – the error variances differ for each value of the predicted score. For each value of the predicted score there would be a different value of the proportionate reduction in error. Therefore, it is inappropriate to think of ² as a proportionate reduction in error in a universal sense in logistic regression.