What is the difference between logistic and logit regression? I understand that they are similar (or even the same thing) but could someone explain the difference(s) between these two? Is one about odds?

  • $\begingroup$ Same thing. In Stata, one gives you the odds ratios, the other gives you the log of the odds ratios. $\endgroup$ Oct 16, 2014 at 17:12
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    $\begingroup$ See Stas K's answer in stats.stackexchange.com/questions/27662/… A short answer is: same thing with different emphases in reporting. $\endgroup$
    – Nick Cox
    Oct 16, 2014 at 17:45
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    $\begingroup$ As with so many things, it depends on who is doing the speaking. Different people use terms in different ways, unfortunately. For example, some people would say they're the same, but other people would use "logistic function" (and hence sometimes even 'a logistic regression') to refer to a nonlinear regression function that's a multiple of the logistic cdf, and which would be a different thing to looking at what's called a logit-link in a GLM. $\endgroup$
    – Glen_b
    Oct 17, 2014 at 0:29

1 Answer 1


The logit is a link function / a transformation of a parameter. It is the logarithm of the odds. If we call the parameter $\pi$, it is defined as follows:
$$ {\rm logit}(\pi) = \log\bigg(\frac{\pi}{1-\pi}\bigg) $$ The logistic function is the inverse of the logit. If we have a value, $x$, the logistic is:
$$ {\rm logistic}(x) = \frac{e^x}{1+e^x} $$ Thus (using matrix notation where $\boldsymbol X$ is an $N\times p$ matrix and $\boldsymbol\beta$ is a $p\times 1$ vector), logit regression is:
$$ \log\bigg(\frac{\pi}{1-\pi}\bigg) = \boldsymbol{X\beta} $$ and logistic regression is:
$$ \pi = \frac{e^\boldsymbol{X\beta}}{1+e^\boldsymbol{X\beta}} $$ For more information about these topics, it may help you to read my answer here: Difference between logit and probit models.

The odds of an event is the probability of the event divided by the probability of the event not occurring. Exponentiating the logit will give the odds. Likewise, you can get the odds by taking the output of the logistic and dividing it by 1 minus the logistic. That is:
$$ {\rm odds} = \exp({\rm logit}(\pi)) = \frac{{\rm logistic}(x)}{1-{\rm logistic}(x)} $$ For more on probabilities and odds, and how logistic regression is related to them, it may help you to read my answer here: Interpretation of simple predictions to odds ratios in logistic regression.


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