Exercise

# Inference with and without outlier (randomization)

Using the randomization test, you can again evaluate the effect of an outlier on the inferential conclusions of a linear model. Run a randomization test on the `hypdata_out`

data twice: once with the outlying value and once without it. Note that the extended lines of code communicate clearly the steps of the randomization tests.

Instructions

**100 XP**

Using the data frames `hypdata_out`

(containing an outlier) and `hypdata_noout`

(outlier removed), the data frames `perm_slope_out`

and `perm_slope_noout`

were created to contain the permuted slopes the original datasets, respectively. The observed values are stored in the variables `obs_slope_out`

and `obs_slope_noout`

.

- Find the p-values by finding the proportion of (
`abs`

olute value) permuted slopes which are larger than or equal to the (`abs`

olute value of the) observed slopes. As before, use`mean`

on the binary inequality to find the proportion.