Is the idea of the CLASSPLOT that if there are lots of Nos above odds ratio 1:1 (ie. logit (p)>0), one should use a different rule to predict the data?
Yes: you might want to predict a ‘Yes’ only with an odds ratio of 2:1, say. Q: In your example, you find that the success rate for trained men is .9 (whose logit is 2.197) and for trained women iit is .955, with a logit of 3.044. Since as you mention in the previous page “What we want to predict from a knowledge of relevant independent variables is…the probability that (the dependent variable) is 1 rather than 0”, what do these logit numbers tell us about the classification of men and women ? A: Standard logistic regression procedure is that if the predicted value of logit(p) for an observation (predicted from the regression equation) is greater than 0.0 (so that the predicted value of p is greater than 0.5), that observation is predicted to lie in group 1; otherwise it is predicted to lie in group 0. This makes sense: p is the probability that the observation is in group 1, so if that is greater than 0.5, we ought to predict membership in group 1. However, examination of the CLASSPL
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