Exercise

# Predicting with glm()

Data scientists often use models to predict future situations. GLMs are one such tool and, when used for these situations, they are sometimes called *supervised learning*.

For this exercise, you will predict the expected number of daily civilian fire injury victims for the North American summer months of June (6), July (7), and August (8) using the Poisson regression you previously fit and the `new_dat`

dataset.

Recall that the Poisson slope and intercept estimates are on the natural log scale and can be exponentiated to be more easily understood.
You can do this by specifying `type = "response"`

with the predict function.

Instructions

**100 XP**

- Print
`new_dat`

to see your new prediction situation. - Use the fit Poisson regression,
`poisson_out`

as the object and`new_dat`

as the new data in`predict()`

. Be sure to exponentiate your output by setting`type = "response"`

. Save the results as`pred_out`

. - Print
`pred_out`

.