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.

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Inference for Linear Regression in R

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Instrucciones del ejercicio

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.

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# Calculate the p-value with the outlier
perm_slope_out %>% 
  mutate(abs_perm_slope = ___) %>%
  summarize(p_value = ___)

# Calculate the p-value without the outlier
perm_slope_noout %>% 
  mutate(abs_perm_slope = ___) %>%
  summarize(p_value = ___)

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