# Polynomial regression with a binary dataset that does not fit logistic regression assumptions

Assumptions. In normal logistic regressions probability has a fixed relationship with the independent variable. It either increases or stays the same as the independent variable increases OR it decreases or stays the same as the independent variable increases. It can't do both. It can't increase as the independent variable increases, and then later decrease (and vice versa).

Suppose I have a data set with a single independent variable and a binary dependent variable where an increase in the independent variable did not always come with an in increase in probability.

Real world example. Let's say youre an expert bowler and we were testing your strike probability after a certain number of warm up shots. You come in on day 1, throw 1 warmup shot and then try to roll a strike. The next day you come in and do 2 warmup shots, adding one warm up shot each day. You'd expect that as the number of warm up shots increases the probability of hitting a strike on your official attempt increases. Clearly though, if the warm up shots continue to increase, at some point your arm get worn out and the strike probability plummets. On day 1000 for example you'd be lucky to granny roll the ball down the lane for your strike attempt

Question. Assuming the independent variable was number of warm up shots and the binary dependent variable is whether or not our bowler rolled a strike after that number of warm ups, could we model strike probability by performing a polynomial regression on the dataset? Is there another more common regression approach for this situation?

• You can use polynomial terms in your logistic regression.
– Dave
Feb 5, 2021 at 15:26