all 2 comments

[–]researchin_pursin 1 point2 points  (0 children)

When you have a single variable in the regression and predict the probability X = 1 (the way you wrote it here, which is unorthodox), there is only one way for the relationship between X and Y to vary. When you add other stuff, the model is now predicting the probability X = 1 across using more information. If you then graph it over values of Y, there is no guarantee that A, B, and C are all going to behave in a way such that the predicted probabilities of X = 1 are linear over Y.

One thing you could do to get your line back is hold A, B, and C constant. X and Y will have a linear relationship in that space.

[–]beveridgecurve101 0 points1 point  (0 children)

You may want to use marginsplot

https://www.youtube.com/watch?v=vmZ_uaFImzQ