Credit should go to Andrew Gelman, who also points out (in his book with Jennifer Hill on hierarchical modeling) that the logistic regression coefficients do have a straightforward interpretation, at least when the probabilities are not too close to the extremes. (I’d have to look it up.)
I don’t have Gelman’s book, but: logistic regression says p = 1 / (1 + exp(-z)) where z is a linear combination of 1 and the independent variables. But then z is just the “log odds”, log(p/(1-p)); you can think of the coefficient of 1 as being the log prior odds ratio and the other coefficients as being the amount of evidence you get for X over not-X per unit change in each independent variable.
Good point. And logistic regression coefficients are hard to interpret, so maybe logistic regression would be a poor choice in this case.
Credit should go to Andrew Gelman, who also points out (in his book with Jennifer Hill on hierarchical modeling) that the logistic regression coefficients do have a straightforward interpretation, at least when the probabilities are not too close to the extremes. (I’d have to look it up.)
I don’t have Gelman’s book, but: logistic regression says p = 1 / (1 + exp(-z)) where z is a linear combination of 1 and the independent variables. But then z is just the “log odds”, log(p/(1-p)); you can think of the coefficient of 1 as being the log prior odds ratio and the other coefficients as being the amount of evidence you get for X over not-X per unit change in each independent variable.