Hi, everyone, and welcome to Module 13.
In this module, we'll look at logistic regression.
Logistic regression is appropriate
when we have a binary or dichotomous outcome.
This is an incredibly useful statistical technique
in public health practice
because we often have binary outcomes.
Things like, do you have an infection or not?
Were you readmitted or not?
Do you have dental caries or not?
All of these things
can be modeled using logistic regression.
Logistic regression allows us to model these binary outcomes
with multiple predictors,
just like we did with multiple linear regression,
so we can start controlling for confounding
and look at effect modification within our models.
In this module, we're going to stick to just one predictor,
which is simple logistic regression.
In the next module,
we'll get into multiple logistic regression,
where we can start to look
at multiple confounders effect modification
and all the things you have already learned
in multiple linear regression.
Let me know if you have any questions, and take care.