# Quadratic terms in logistic regression

I am looking at the results of a logistic regression model (i dont have the data) and the person who has developed the model has included quadratic terms in the model.

I understand the use of such polynomial terms in a linear model where one can look at the relationship between the response and the predictor. But in case of a binary outcome, is there a way to identify such a trend before hand i.e. without including it in the model and then checking if the variable is significant or not?

• There is no way to separately interpret a coefficient for a "quadratic term". It is essentially an interaction and requires that you make a prediction across the range of possible values using all terms that contribute to the variation of the outcome. The notion of a quadratic terms for a binary variable isn't making a lot of sense to me.
– DWin
Mar 7, 2016 at 0:28
• @DWin I believe the OP means a quadratic expansion of a continuous covariate in logistic regression. In others, if $x$ is a predictor of $y$, include $x$ and $x^2$ as two covariates. Mar 7, 2016 at 3:45
• That is what I interpreted the question as requesting. But I hope you agree that it remains unclear what a "quadratic trend" might mean. There's no way to decide whether a positive sign for a squared term actually means a concave upward "trend" in the region of domain of interest. My main point is that one needs to predict using the (Intercep, linear and quadratic coefficients together over the range of interest. Attempting to assign meaning to individual coefficients is foolish.
– DWin
Mar 7, 2016 at 4:55
• @DWin: I agree the word "trend" is a little misleading, but like Ciff has mentioned what i meant was how does one figure out whether to add a x^2 or X^n predictor in the model if we can't get a good feel of the relationship between the predictor and outcome. I get your point of trial and check but my doubt is what would prompt someone to try a polynomial covariate?
– Raj
Mar 7, 2016 at 15:49