Exercise

# Linear regression imputation

Sometimes, you can use domain knowledge, previous research or simply your common sense to describe the relations between the variables in your data. In such cases, model-based imputation is a great solution, as it allows you to impute each variable according to a statistical model that you can specify yourself, taking into account any assumptions you might have about how the variables impact each other.

For continuous variables, a popular model choice is linear regression. It doesn't restrict you to linear relations though! You can always include a square or a logarithm of a variable in the predictors. In this exercise, you will work with the `simputation`

package to run a single linear regression imputation on the `tao`

data and analyze the results. Let's give it a try!

Instructions 1/4

**undefined XP**

- Load the
`simputation`

package. - Use
`impute_lm()`

to perform a linear regression imputation of`air_temp`

and`humidity`

, while using`year`

,`latitude`

and`sea_surface_temp`

as predictors and assign the result to`tao_imp`

.