This produces a relationship similar to that in multiple regression except that now each one-unit change in a predictor is associated with a change in log odds rather than the response directly. As with multiple regression it is also possible to test a range of models by applying stepwise inclusion or elimination of predictors. Interpretation of the coefficients is complicated by the fact that they relate to changes in log odds rather than the response itself. There is a non-linear relationship between p and its logit. In the mid range of p there is a linear relationship, but as p approaches the 0 or 1 extremes the relationship becomes non-linear with increasingly larger changes in logit for the same change in p .
More detailed descriptions are available from the sources listed in Resources .
Ex amples from the literature. Boerlin, P, McEwen, SA, BoerlinPetzold, F, Wilson, JB, Johnson, RP, Gyles, CL. 1999. Associations between virulence factors of Shiga toxin-producing Escherichia coli and disease in humans. Cocaine use and high-risk sexual behavior among STD clinic patients. Valeri, A, Joseph, R, Radhakrishnan, J 1999.