@FreelanceReinh , Thank you for a wonderful reply. This is making a difficult concept a lot more clear. Can you please clarify two quotes: Yes, in both models (with and without intercept) this number estimates the difference between those logits, i.e., the log-odds ratio of level 1 vs. level 2,everything else(i.e. the value of the second predictor)being the same. Do you mean everything else being zero, like you answered for Scenario 2 ("the first predictor being at its reference level and the second predictor being equal to zero")? In that case, the coefficient_1 for these two models would be the same (D_level1 is the design variable for level1 of categorical predictor): (1) logit(predicted probability of event) = intercept + coefficient_1*D_level1 (2) logit(predicted probability of event) = intercept + coefficient_1*D_level1 + coefficient_2*continuous_predictor But, in fact, coefficient_1 changes with the addition of the continuous predictor. Also with the intercept, coefficient_1 changes with the addition of the continuous predictor. How would you explain that? However, if the true intercept is not zero and the NOINT option is used inappropriately, the number in question will likely overestimate or underestimate that log odds ratio. Is NOINT only appropriate for a model that contains only the continuous predictors? It seems that with addition of categorical factors, the intercept becomes a necessity, because it has a meaning. For example, it is not appropriate for me to change the predicted probability of the reference level (level2) from 0.3785 to 0.5000 like I did by using the NOINT option on model (1). How would you tell if a true intercept is not zero? When would you say it is inappropriate to use the NOINT option? Thank you very much for your help.
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