Exercise

# Making probabilistic predictions

Just as we did with linear regression, we can use our logistic regression model to make predictions about new observations. In this exercise, we will use the `newdata`

argument to the `augment()`

function from the `broom`

package to make predictions about students who were not in our original data set. These predictions are sometimes called *out-of-sample*.

Following our previous discussion about scales, with logistic regression it is important that we specify on which scale we want the predicted values. Although the default is `terms`

-- which uses the log-odds scale -- we want our predictions on the probability scale, which is the scale of the `response`

variable. The `type.predict`

argument to `augment()`

controls this behavior.

A logistic regression model object, `mod`

, has been defined for you.

Instructions

**100 XP**

- Create a new data frame which has one variable called
`GPA`

and one row, with the value 3.51. - Use
`augment()`

to find the expected probability of admission to medical school for a student with a GPA of 3.51.