Logistic regression is a generalized linear model for binomially distributed data which uses a logit function link.

Stata offers multiple commands for running a logistic regression. `glm`

and `logit`

report log-odds coefficients while `logistic`

reports odds ratios. `logit`

is just a shortcut for `glm`

to save typing out the family and link.

```
glm x_d1 y z, family(binomial) link(logit)
logit x_d1 y z
logistic x_d1 y z
```

R uses `glm()`

for logistic regression.

```
example_model2 <-
glm(x_d1 ~ y + z,
family = binomial(link = "logit"),
data = example_data)
summary(example_model2)
```

To obtain odds ratios, exponentiate the log-odds coefficients and/or their confidence intervals:

```
exp(coef(example_model2))
exp(confint(example_model2))
```