Skip to content

Update inf-model-logistic.qmd #511

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update inf-model-logistic.qmd #511

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion inf-model-logistic.qmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ email_variables |>

Before looking at the hypothesis tests associated with the coefficients (turns out they are very similar to those in linear regression!), it is valuable to understand the technical conditions that underlie the inference applied to the logistic regression model.
Generally, as you've seen in the logistic regression modeling examples, it is imperative that the response variable is binary.
Additionally, the key technical condition for logistic regression has to do with the relationship between the predictor variables $(x_i$ values) and the probability the outcome will be a success.
Additionally, the key technical condition for logistic regression has to do with the relationship between the predictor variables ($x_i$ values) and the probability the outcome will be a success.
It turns out, the relationship is a specific functional form called a logit function, where ${\rm logit}(p) = \log_e(\frac{p}{1-p}).$ The function may feel complicated, and memorizing the formula of the logit is not necessary for understanding logistic regression.
What you do need to remember is that the probability of the outcome being a success is a function of a linear combination of the explanatory variables.

Expand Down
Loading