In logistic regression, logit Y = ax1+ bx2 +cx3. a variable has a coefficient attached to it. So we can directly measure the change in y per unit change in x. Can I get a similar relationship from odds ratio? per unit change in x contributing to how much change in y. Basically I want to understand the contribution to score. E.g. a variable has odds of 20:1 compared to 6:1


1 Answer 1


Your first line is incorrect. In a logistic regression, Y is not linear in the parameters. Indeed, that's one reason why we need logistic regression. The logit of y is linear in the parameters.

The parameters output from a logistic regression lack an intuitive explanation, unless your intuition about equations with exponentials in both numerator and denominator are a whole lot better than mine.

The odds ratio does what you think the parameter does. That is, the OR measures how the odds of Y change per unit increase in each X.

  • $\begingroup$ sorry.. i meant logit of y. $\endgroup$ Commented Feb 11, 2016 at 12:41
  • $\begingroup$ sorry.. i meant logit of y. my equation is a linear regression. say i had 10 variables in the equation. the top one had an odds ratio of 20:1 while the lowest one is 6:1. But I intuitively feel that the lowest one is more valid and I have only got these odds due to lack of training data. Should I favor the top variable compared to the lower. I just want to understand how much difference can result in output. something similar to linear regression. $\endgroup$ Commented Feb 11, 2016 at 12:51
  • $\begingroup$ I don't think this is at all the way to build models. $\endgroup$
    – Peter Flom
    Commented Feb 11, 2016 at 18:06
  • $\begingroup$ if you have quality data then definitely not, however if you have data challenges, especially in text mining, i think you have to be on the lookout for potential glitches and trust common sense rather than blindly following a model. $\endgroup$ Commented Feb 12, 2016 at 2:56

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