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.

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 ( absolute value) permuted slopes which are larger than or equal to the ( absolute value of the) observed slopes. As before, use mean on the binary inequality to find the proportion.

Exercise

Inference with and without outlier (randomization)

Instructions